{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f355316a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/usr/local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/usr/local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/usr/local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/usr/local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/usr/local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/usr/local/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from gernerate_data import load_clas_seg_data\n",
    "import tensorflow as tf\n",
    "from sklearn.metrics import classification_report, auc, roc_curve\n",
    "from keras.utils.np_utils import *\n",
    "from keras.callbacks import LearningRateScheduler\n",
    "from models.MTL_IBA import MTL_IBA, MTL_IBA_h3\n",
    "from models.MLT_net import MTL_classic\n",
    "# from models.shareLayer import create_pair_model\n",
    "from sklearn.preprocessing import LabelBinarizer, label_binarize\n",
    "from utils.losses import dice_coef_loss, dice_coef, dice_p_bce, dice_p_focal, tversky_loss, focal_loss, focal_tversky, \\\n",
    "    p_r_f1_iou, generalized_dice_coeff, generalized_dice_loss\n",
    "from keras.losses import categorical_crossentropy, mean_squared_error, binary_crossentropy\n",
    "from keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, EarlyStopping, CSVLogger\n",
    "from keras.optimizers import Adam\n",
    "import matplotlib.pyplot as plt\n",
    "import keras.backend as K\n",
    "import utils_paths\n",
    "import numpy as np\n",
    "import pickle\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3ed814ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------------------ Preparing model ------------------------------------------------\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-03-29 13:43:25.569338: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA\n",
      "2022-03-29 13:43:25.605194: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2200095000 Hz\n",
      "2022-03-29 13:43:25.609697: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x6f9e060 executing computations on platform Host. Devices:\n",
      "2022-03-29 13:43:25.609817: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): <undefined>, <undefined>\n",
      "2022-03-29 13:43:26.537646: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x704a0a0 executing computations on platform CUDA. Devices:\n",
      "2022-03-29 13:43:26.537737: I tensorflow/compiler/xla/service/service.cc:158]   StreamExecutor device (0): Tesla V100-PCIE-32GB, Compute Capability 7.0\n",
      "2022-03-29 13:43:26.538845: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0 with properties: \n",
      "name: Tesla V100-PCIE-32GB major: 7 minor: 0 memoryClockRate(GHz): 1.38\n",
      "pciBusID: 0000:05:00.0\n",
      "totalMemory: 31.72GiB freeMemory: 17.84GiB\n",
      "2022-03-29 13:43:26.538884: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible gpu devices: 0\n",
      "2022-03-29 13:43:26.541631: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
      "2022-03-29 13:43:26.541673: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990]      0 \n",
      "2022-03-29 13:43:26.541689: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0:   N \n",
      "2022-03-29 13:43:26.542344: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 17356 MB memory) -> physical GPU (device: 0, name: Tesla V100-PCIE-32GB, pci bus id: 0000:05:00.0, compute capability: 7.0)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 224, 224, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv1 (Conv2D)           (None, 224, 224, 64) 1792        input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv2 (Conv2D)           (None, 224, 224, 64) 36928       block1_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block1_pool (MaxPooling2D)      (None, 112, 112, 64) 0           block1_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block2_conv1 (Conv2D)           (None, 112, 112, 128 73856       block1_pool[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block2_conv2 (Conv2D)           (None, 112, 112, 128 147584      block2_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block2_pool (MaxPooling2D)      (None, 56, 56, 128)  0           block2_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block3_conv1 (Conv2D)           (None, 56, 56, 256)  295168      block2_pool[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block3_conv2 (Conv2D)           (None, 56, 56, 256)  590080      block3_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block3_conv3 (Conv2D)           (None, 56, 56, 256)  590080      block3_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block3_pool (MaxPooling2D)      (None, 28, 28, 256)  0           block3_conv3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block4_conv1 (Conv2D)           (None, 28, 28, 512)  1180160     block3_pool[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block4_conv2 (Conv2D)           (None, 28, 28, 512)  2359808     block4_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block4_conv3 (Conv2D)           (None, 28, 28, 512)  2359808     block4_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block4_pool (MaxPooling2D)      (None, 14, 14, 512)  0           block4_conv3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block5_conv1 (Conv2D)           (None, 14, 14, 512)  2359808     block4_pool[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "block5_conv2 (Conv2D)           (None, 14, 14, 512)  2359808     block5_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block5_conv3 (Conv2D)           (None, 14, 14, 512)  2359808     block5_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_1 (UpSampling2D)  (None, 28, 28, 512)  0           block5_conv3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 28, 28, 1024) 0           block4_conv3[0][0]               \n",
      "                                                                 up_sampling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 28, 28, 512)  4719104     concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 28, 28, 512)  2048        conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 28, 28, 512)  0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_2 (UpSampling2D)  (None, 56, 56, 512)  0           activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_2 (Concatenate)     (None, 56, 56, 768)  0           block3_conv3[0][0]               \n",
      "                                                                 up_sampling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 56, 56, 256)  1769728     concatenate_2[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 56, 56, 256)  1024        conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 56, 56, 256)  0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_3 (UpSampling2D)  (None, 112, 112, 256 0           activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_3 (Concatenate)     (None, 112, 112, 384 0           block2_conv2[0][0]               \n",
      "                                                                 up_sampling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 112, 112, 128 442496      concatenate_3[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 112, 112, 128 512         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 112, 112, 128 0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "up_sampling2d_4 (UpSampling2D)  (None, 224, 224, 128 0           activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_4 (Concatenate)     (None, 224, 224, 192 0           block1_conv2[0][0]               \n",
      "                                                                 up_sampling2d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 224, 224, 64) 110656      concatenate_4[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "block5_pool (MaxPooling2D)      (None, 7, 7, 512)    0           block5_conv3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 224, 224, 64) 256         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "cls_flatten (Flatten)           (None, 25088)        0           block5_pool[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 224, 224, 64) 0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "cls_dense_0 (Dense)             (None, 64)           1605696     cls_flatten[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 224, 224, 64) 36928       activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "cls_act_3 (Activation)          (None, 64)           0           cls_dense_0[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 224, 224, 64) 256         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "cls_dropout (Dropout)           (None, 64)           0           cls_act_3[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 224, 224, 64) 0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "cls_dense_out (Dense)           (None, 2)            130         cls_dropout[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 224, 224, 2)  130         activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "cls_act_4 (Activation)          (None, 2)            0           cls_dense_out[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 224, 224, 2)  0           conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "classification_output (Activati (None, 2)            0           cls_act_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "segmentation_output (Activation (None, 224, 224, 2)  0           activation_6[0][0]               \n",
      "==================================================================================================\n",
      "Total params: 23,403,652\n",
      "Trainable params: 23,401,604\n",
      "Non-trainable params: 2,048\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "INIT_LR = 2e-4\n",
    "EPOCHS =200\n",
    "batch_size = 8\n",
    "depth = 3\n",
    "img_size = 224\n",
    "Name = \"MTL_classic\"\n",
    "GPU = True\n",
    "target = (img_size, img_size)\n",
    "\n",
    "if GPU:\n",
    "    os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n",
    "    os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n",
    "\n",
    "\n",
    "    \n",
    "print(\"------------------------------------------------ Preparing model ------------------------------------------------\")\n",
    "# model = MTL_3(img_size, img_size, depth, nClasses=2)\n",
    "# model = MTL_IBA(img_size, img_size, depth, nClasses=2)\n",
    "# model = MTL_IBA_h3(img_size, img_size, depth, nClasses=2)\n",
    "model = MTL_classic(img_size, img_size, depth, nClasses=2)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f7479810",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dataset/Dataset_BUSI_AN/train/images/bengin_images/benign (337)_1a541313-68f8-4236-86da-3a7f4c227d83.png\n",
      "dataset/Dataset_BUSI_AN/train/masks/bengin_masks/benign (337)_1a541313-68f8-4236-86da-3a7f4c227d83.png\n",
      "dataset/Dataset_BUSI_AN/train/images/bengin_images/benign (396).png\n",
      "dataset/Dataset_BUSI_AN/train/masks/bengin_masks/benign (396).png\n",
      "dataset/Dataset_BUSI_AN/train/images/bengin_images/benign (435).png\n",
      "dataset/Dataset_BUSI_AN/train/masks/bengin_masks/benign (435).png\n",
      "dataset/Dataset_BUSI_AN/train/images/bengin_images/benign (91)_755a6075-3551-47bc-9a8f-e6919826a078.png\n",
      "dataset/Dataset_BUSI_AN/train/masks/bengin_masks/benign (91)_755a6075-3551-47bc-9a8f-e6919826a078.png\n",
      "dataset/Dataset_BUSI_AN/train/images/bengin_images/benign (214).png\n",
      "dataset/Dataset_BUSI_AN/train/masks/bengin_masks/benign (214).png\n",
      "dataset/Dataset_BUSI_AN/train/images/bengin_images/benign (415).png\n",
      "dataset/Dataset_BUSI_AN/train/masks/bengin_masks/benign (415).png\n",
      "dataset/Dataset_BUSI_AN/train/images/bengin_images/benign (369).png\n",
      "dataset/Dataset_BUSI_AN/train/masks/bengin_masks/benign (369).png\n",
      "dataset/Dataset_BUSI_AN/train/images/bengin_images/benign (190).png\n",
      "dataset/Dataset_BUSI_AN/train/masks/bengin_masks/benign (190).png\n",
      "dataset/Dataset_BUSI_AN/train/images/bengin_images/benign (423).png\n",
      "dataset/Dataset_BUSI_AN/train/masks/bengin_masks/benign (423).png\n",
      "dataset/Dataset_BUSI_AN/train/images/bengin_images/benign (416).png\n",
      "dataset/Dataset_BUSI_AN/train/masks/bengin_masks/benign (416).png\n",
      "dataset/Dataset_BUSI_AN/train/images/bengin_images/benign (37)_fa52b803-6287-46be-9221-8b935ba6f16b.png\n",
      "dataset/Dataset_BUSI_AN/train/masks/bengin_masks/benign (37)_fa52b803-6287-46be-9221-8b935ba6f16b.png\n",
      "dataset/Dataset_BUSI_AN/train/images/bengin_images/benign (31)_743673fa-0499-4f23-93a0-e4b83c22df9d.png\n",
      "dataset/Dataset_BUSI_AN/train/masks/bengin_masks/benign (31)_743673fa-0499-4f23-93a0-e4b83c22df9d.png\n",
      "dataset/Dataset_BUSI_AN/train/images/bengin_images/benign (280).png\n",
      "dataset/Dataset_BUSI_AN/train/masks/bengin_masks/benign (280).png\n",
      "dataset/Dataset_BUSI_AN/train/images/bengin_images/benign (246).png\n",
      "dataset/Dataset_BUSI_AN/train/masks/bengin_masks/benign (246).png\n",
      "dataset/Dataset_BUSI_AN/train/images/bengin_images/benign (49)_8180aeae-69a8-4438-867c-daa4c98bd972.png\n",
      "dataset/Dataset_BUSI_AN/train/masks/bengin_masks/benign (49)_8180aeae-69a8-4438-867c-daa4c98bd972.png\n",
      "dataset/Dataset_BUSI_AN/train/images/bengin_images/benign (120).png\n",
      "dataset/Dataset_BUSI_AN/train/masks/bengin_masks/benign (120).png\n",
      "dataset/Dataset_BUSI_AN/train/images/bengin_images/benign (431)_d7c3d11e-b352-4e23-bcda-b534b3588de0.png\n",
      "dataset/Dataset_BUSI_AN/train/masks/bengin_masks/benign (431)_d7c3d11e-b352-4e23-bcda-b534b3588de0.png\n",
      "dataset/Dataset_BUSI_AN/train/images/bengin_images/benign (131).png\n",
      "dataset/Dataset_BUSI_AN/train/masks/bengin_masks/benign (131).png\n",
      "dataset/Dataset_BUSI_AN/train/images/bengin_images/benign (174).png\n",
      "dataset/Dataset_BUSI_AN/train/masks/bengin_masks/benign (174).png\n",
      "dataset/Dataset_BUSI_AN/train/images/bengin_images/benign (7).png\n",
      "dataset/Dataset_BUSI_AN/train/masks/bengin_masks/benign (7).png\n",
      "dataset/Dataset_BUSI_AN/train/images/bengin_images/benign (40)_e9445a37-3abf-4b84-a6aa-b3addc9cf237.png\n",
      "dataset/Dataset_BUSI_AN/train/masks/bengin_masks/benign (40)_e9445a37-3abf-4b84-a6aa-b3addc9cf237.png\n",
      "dataset/Dataset_BUSI_AN/train/images/bengin_images/benign (6).png\n",
      "dataset/Dataset_BUSI_AN/train/masks/bengin_masks/benign (6).png\n",
      "dataset/Dataset_BUSI_AN/train/images/bengin_images/benign (100)_f2a76c94-44e4-4331-948b-c6c0a04969b4.png\n",
      "dataset/Dataset_BUSI_AN/train/masks/bengin_masks/benign (100)_f2a76c94-44e4-4331-948b-c6c0a04969b4.png\n",
      "dataset/Dataset_BUSI_AN/train/images/bengin_images/benign (90)_bcb13c29-34ab-4775-b1a3-fefe90a12743.png\n",
      "dataset/Dataset_BUSI_AN/train/masks/bengin_masks/benign (90)_bcb13c29-34ab-4775-b1a3-fefe90a12743.png\n",
      "dataset/Dataset_BUSI_AN/train/images/bengin_images/benign (5).png\n",
      "dataset/Dataset_BUSI_AN/train/masks/bengin_masks/benign (5).png\n",
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      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (104).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (15).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (15).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (88).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (88).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (140).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (140).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (103).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (103).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (21).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (21).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (231).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (231).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (349).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (349).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (222).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (222).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (408).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (408).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (81).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (81).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (155).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (155).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (51).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (51).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (119).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (119).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (388).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (388).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (221).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (221).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (218).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (218).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (324).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (324).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (127).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (127).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (364).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (364).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (353).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (353).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (167).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (167).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (153).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (153).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (142).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (142).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (150).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (150).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (425).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (425).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (136).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (136).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (154).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (154).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (38).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (38).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (201).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (201).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (187).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (187).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (220).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (220).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (299).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (299).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (147).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (147).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (315).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (315).png\n",
      "dataset/Dataset_BUSI_AN/test/images/bengin_images/benign (84).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/bengin_masks/benign (84).png\n",
      "dataset/Dataset_BUSI_AN/test/images/malignant_images/malignant (74).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/malignant_masks/malignant (74).png\n",
      "dataset/Dataset_BUSI_AN/test/images/malignant_images/malignant (121).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/malignant_masks/malignant (121).png\n",
      "dataset/Dataset_BUSI_AN/test/images/malignant_images/malignant (72).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/malignant_masks/malignant (72).png\n",
      "dataset/Dataset_BUSI_AN/test/images/malignant_images/malignant (131).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/malignant_masks/malignant (131).png\n",
      "dataset/Dataset_BUSI_AN/test/images/malignant_images/malignant (136).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/malignant_masks/malignant (136).png\n",
      "dataset/Dataset_BUSI_AN/test/images/malignant_images/malignant (171).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/malignant_masks/malignant (171).png\n",
      "dataset/Dataset_BUSI_AN/test/images/malignant_images/malignant (191).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/malignant_masks/malignant (191).png\n",
      "dataset/Dataset_BUSI_AN/test/images/malignant_images/malignant (123).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/malignant_masks/malignant (123).png\n",
      "dataset/Dataset_BUSI_AN/test/images/malignant_images/malignant (86).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/malignant_masks/malignant (86).png\n",
      "dataset/Dataset_BUSI_AN/test/images/malignant_images/malignant (18).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/malignant_masks/malignant (18).png\n",
      "dataset/Dataset_BUSI_AN/test/images/malignant_images/malignant (146).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/malignant_masks/malignant (146).png\n",
      "dataset/Dataset_BUSI_AN/test/images/malignant_images/malignant (44).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/malignant_masks/malignant (44).png\n",
      "dataset/Dataset_BUSI_AN/test/images/malignant_images/malignant (190).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/malignant_masks/malignant (190).png\n",
      "dataset/Dataset_BUSI_AN/test/images/malignant_images/malignant (65).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/malignant_masks/malignant (65).png\n",
      "dataset/Dataset_BUSI_AN/test/images/malignant_images/malignant (149).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/malignant_masks/malignant (149).png\n",
      "dataset/Dataset_BUSI_AN/test/images/malignant_images/malignant (112).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/malignant_masks/malignant (112).png\n",
      "dataset/Dataset_BUSI_AN/test/images/malignant_images/malignant (20).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/malignant_masks/malignant (20).png\n",
      "dataset/Dataset_BUSI_AN/test/images/malignant_images/malignant (200).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/malignant_masks/malignant (200).png\n",
      "dataset/Dataset_BUSI_AN/test/images/malignant_images/malignant (45).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/malignant_masks/malignant (45).png\n",
      "dataset/Dataset_BUSI_AN/test/images/malignant_images/malignant (106).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/malignant_masks/malignant (106).png\n",
      "dataset/Dataset_BUSI_AN/test/images/malignant_images/malignant (170).png\n",
      "dataset/Dataset_BUSI_AN/test/masks/malignant_masks/malignant (170).png\n"
     ]
    }
   ],
   "source": [
    "# trainX_dir = 'dataset/LE/test/images/'\n",
    "trainX_dir = 'dataset/Dataset_BUSI_AN/train/images/'\n",
    "valX_dir = trainX_dir.replace('train', 'val')\n",
    "testX_dir = trainX_dir.replace('train', 'test')\n",
    "\n",
    "\n",
    "train_x,  train_c_y, train_s_y = load_clas_seg_data(trainX_dir, target)\n",
    "val_x, val_c_y, val_s_y = load_clas_seg_data(valX_dir, target)\n",
    "test_x, test_c_y, test_s_y = load_clas_seg_data(testX_dir, target)\n",
    "\n",
    "\n",
    "lb = LabelBinarizer()\n",
    "train_c_y = lb.fit_transform(train_c_y)\n",
    "val_c_y = lb.fit_transform(val_c_y)\n",
    "test_c_y = lb.fit_transform(test_c_y)\n",
    "train_c_y = to_categorical(train_c_y, 2)\n",
    "val_c_y = to_categorical(val_c_y, 2)\n",
    "test_c_y = to_categorical(test_c_y, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c6931d5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from models.shareLayer import create_pair_model\n",
    "# from models.MLT_net import MTL_classic, MTL_3\n",
    "\n",
    "# model = MTL_classic(img_size, img_size, depth, nClasses=2)\n",
    "# model = MTL_3(img_size, img_size, depth, nClasses=2)\n",
    "# model = create_pair_model(img_size, img_size, depth, nClasses=2)\n",
    "# model.summary()\n",
    "\n",
    "\n",
    "# define callbacks\n",
    "csv_logger = CSVLogger(Name+'.log')\n",
    "\n",
    "reduce_lr = ReduceLROnPlateau(monitor='classification_output_loss', factor=0.2, patience=20, min_lr=1e-8, mode='auto', verbose=1)\n",
    "\n",
    "checkpoint_period1 = ModelCheckpoint(Name + '-{epoch:03d}-{classification_output_acc:.4f}.h5',\n",
    "                                     monitor='classification_output_acc', mode='auto', save_best_only='True')\n",
    "\n",
    "\n",
    "\n",
    "# define loss and compile the model\n",
    "\n",
    "opt = Adam(lr=INIT_LR, beta_1=0.9, beta_2=0.99, epsilon=1e-08, decay=0.01)\n",
    "\n",
    "def binary_crossentropy(y_true, y_pred):\n",
    "    e=1.0\n",
    "    return K.mean(-(y_true*K.log(y_pred+K.epsilon())+\n",
    "                    e*(1-y_true)*K.log(1-y_pred+K.epsilon())),\n",
    "                  axis=-1)\n",
    "\n",
    "def tversky(y_true, y_pred, smooth=1):\n",
    "    y_true_pos = K.flatten(y_true)\n",
    "    y_pred_pos = K.flatten(y_pred)\n",
    "    true_pos = K.sum(y_true_pos * y_pred_pos)\n",
    "    false_neg = K.sum(y_true_pos * (1 - y_pred_pos))\n",
    "    false_pos = K.sum((1 - y_true_pos) * y_pred_pos)\n",
    "    alpha = 0.5\n",
    "    return (true_pos + smooth) / (true_pos + alpha * false_neg + (1 - alpha) * false_pos + smooth)\n",
    "\n",
    "def tversky_loss(y_true, y_pred):\n",
    "    return 1 - tversky(y_true, y_pred)\n",
    "\n",
    "\n",
    "from keras.losses import categorical_crossentropy\n",
    "\n",
    "# model.compile(loss={'segmentation_output': categorical_crossentropy, 'seg-out': tversky_loss, \"classification_output\": binary_crossentropy},\n",
    "#               loss_weights={'segmentation_output': 0.25,'seg-out': 0.5, \"classification_output\": 0.5},\n",
    "#               optimizer=opt, \n",
    "#               metrics={'segmentation_output':'accuracy', 'seg-out':dice_coef, 'classification_output':'accuracy'})\n",
    "\n",
    "# model.compile(loss={'segmentation_output': categorical_crossentropy, 'seg-out': tversky_loss, \"classification_output\": binary_crossentropy},\n",
    "#               loss_weights={'segmentation_output': 0.25,'seg-out': 0.5, \"classification_output\": 0.5},\n",
    "#               optimizer=opt, \n",
    "#               metrics={'segmentation_output':'accuracy', 'seg-out':dice_coef, 'classification_output':'accuracy'})\n",
    "\n",
    "# # model.compile(loss={'segmentation_output': focal_tversky, \"classification_output\": binary_crossentropy},\n",
    "model.compile(loss={'segmentation_output': tversky_loss, \"classification_output\": binary_crossentropy},\n",
    "              loss_weights={'segmentation_output': 0.5, \"classification_output\": 0.5},\n",
    "              optimizer=opt, \n",
    "              metrics={'segmentation_output':['accuracy', dice_coef], 'classification_output':'accuracy'})\n",
    "\n",
    "# model.compile(loss={'seg_out': 'categorical_crossentropy', 'cls_out': binary_crossentropy},\n",
    "#               optimizer=Adam(lr=1e-4), metrics={'seg_out':dice_coef, 'cls_out':'accuracy'},\n",
    "#               loss_weights={'seg_out':0.5, 'cls_out':0.5})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "49cf0bf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================ begin training ================================================\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/tensorflow/python/ops/math_grad.py:102: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Deprecated in favor of operator or tf.math.divide.\n",
      "Train on 1000 samples, validate on 64 samples\n",
      "Epoch 1/200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-03-29 13:43:58.673299: I tensorflow/stream_executor/dso_loader.cc:152] successfully opened CUDA library libcublas.so.10.0 locally\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000/1000 [==============================] - 66s 66ms/step - loss: 0.4521 - classification_output_loss: 0.6959 - segmentation_output_loss: 0.2083 - classification_output_acc: 0.5020 - segmentation_output_acc: 0.8788 - segmentation_output_dice_coef: 0.7917 - val_loss: 0.4238 - val_classification_output_loss: 0.6931 - val_segmentation_output_loss: 0.1545 - val_classification_output_acc: 0.6719 - val_segmentation_output_acc: 0.9146 - val_segmentation_output_dice_coef: 0.8455\n",
      "Epoch 2/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.4206 - classification_output_loss: 0.6953 - segmentation_output_loss: 0.1458 - classification_output_acc: 0.5090 - segmentation_output_acc: 0.8829 - segmentation_output_dice_coef: 0.8542 - val_loss: 0.4022 - val_classification_output_loss: 0.6931 - val_segmentation_output_loss: 0.1113 - val_classification_output_acc: 0.6719 - val_segmentation_output_acc: 0.9146 - val_segmentation_output_dice_coef: 0.8887\n",
      "Epoch 3/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.4127 - classification_output_loss: 0.6933 - segmentation_output_loss: 0.1320 - classification_output_acc: 0.5000 - segmentation_output_acc: 0.8829 - segmentation_output_dice_coef: 0.8680 - val_loss: 0.4019 - val_classification_output_loss: 0.6931 - val_segmentation_output_loss: 0.1106 - val_classification_output_acc: 0.6719 - val_segmentation_output_acc: 0.9146 - val_segmentation_output_dice_coef: 0.8894\n",
      "Epoch 4/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.4093 - classification_output_loss: 0.6939 - segmentation_output_loss: 0.1248 - classification_output_acc: 0.4930 - segmentation_output_acc: 0.8830 - segmentation_output_dice_coef: 0.8752 - val_loss: 0.3979 - val_classification_output_loss: 0.6931 - val_segmentation_output_loss: 0.1027 - val_classification_output_acc: 0.6719 - val_segmentation_output_acc: 0.9160 - val_segmentation_output_dice_coef: 0.8973\n",
      "Epoch 5/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.4062 - classification_output_loss: 0.6933 - segmentation_output_loss: 0.1190 - classification_output_acc: 0.5010 - segmentation_output_acc: 0.8845 - segmentation_output_dice_coef: 0.8810 - val_loss: 0.3966 - val_classification_output_loss: 0.6931 - val_segmentation_output_loss: 0.1001 - val_classification_output_acc: 0.6719 - val_segmentation_output_acc: 0.9206 - val_segmentation_output_dice_coef: 0.8999\n",
      "Epoch 6/200\n",
      "1000/1000 [==============================] - 37s 37ms/step - loss: 0.3967 - classification_output_loss: 0.6934 - segmentation_output_loss: 0.1001 - classification_output_acc: 0.5050 - segmentation_output_acc: 0.9230 - segmentation_output_dice_coef: 0.8999 - val_loss: 0.3846 - val_classification_output_loss: 0.6931 - val_segmentation_output_loss: 0.0760 - val_classification_output_acc: 0.6719 - val_segmentation_output_acc: 0.9449 - val_segmentation_output_dice_coef: 0.9240\n",
      "Epoch 7/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.3894 - classification_output_loss: 0.6934 - segmentation_output_loss: 0.0854 - classification_output_acc: 0.5010 - segmentation_output_acc: 0.9384 - segmentation_output_dice_coef: 0.9146 - val_loss: 0.3884 - val_classification_output_loss: 0.6931 - val_segmentation_output_loss: 0.0837 - val_classification_output_acc: 0.6719 - val_segmentation_output_acc: 0.9358 - val_segmentation_output_dice_coef: 0.9163\n",
      "Epoch 8/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.3859 - classification_output_loss: 0.6922 - segmentation_output_loss: 0.0795 - classification_output_acc: 0.5030 - segmentation_output_acc: 0.9426 - segmentation_output_dice_coef: 0.9205 - val_loss: 0.3812 - val_classification_output_loss: 0.6931 - val_segmentation_output_loss: 0.0692 - val_classification_output_acc: 0.6719 - val_segmentation_output_acc: 0.9505 - val_segmentation_output_dice_coef: 0.9308\n",
      "Epoch 9/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.3773 - classification_output_loss: 0.6762 - segmentation_output_loss: 0.0783 - classification_output_acc: 0.4970 - segmentation_output_acc: 0.9431 - segmentation_output_dice_coef: 0.9217 - val_loss: 0.3330 - val_classification_output_loss: 0.5959 - val_segmentation_output_loss: 0.0701 - val_classification_output_acc: 0.6719 - val_segmentation_output_acc: 0.9482 - val_segmentation_output_dice_coef: 0.9299\n",
      "Epoch 10/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.3544 - classification_output_loss: 0.6349 - segmentation_output_loss: 0.0739 - classification_output_acc: 0.5000 - segmentation_output_acc: 0.9463 - segmentation_output_dice_coef: 0.9261 - val_loss: 0.3169 - val_classification_output_loss: 0.5668 - val_segmentation_output_loss: 0.0669 - val_classification_output_acc: 0.6719 - val_segmentation_output_acc: 0.9522 - val_segmentation_output_dice_coef: 0.9331\n",
      "Epoch 11/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.3484 - classification_output_loss: 0.6258 - segmentation_output_loss: 0.0709 - classification_output_acc: 0.5080 - segmentation_output_acc: 0.9487 - segmentation_output_dice_coef: 0.9291 - val_loss: 0.3170 - val_classification_output_loss: 0.5681 - val_segmentation_output_loss: 0.0658 - val_classification_output_acc: 0.6719 - val_segmentation_output_acc: 0.9522 - val_segmentation_output_dice_coef: 0.9342\n",
      "Epoch 12/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.3398 - classification_output_loss: 0.6116 - segmentation_output_loss: 0.0681 - classification_output_acc: 0.5080 - segmentation_output_acc: 0.9505 - segmentation_output_dice_coef: 0.9319 - val_loss: 0.3105 - val_classification_output_loss: 0.5549 - val_segmentation_output_loss: 0.0662 - val_classification_output_acc: 0.6719 - val_segmentation_output_acc: 0.9528 - val_segmentation_output_dice_coef: 0.9338\n",
      "Epoch 13/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.3340 - classification_output_loss: 0.6008 - segmentation_output_loss: 0.0671 - classification_output_acc: 0.5050 - segmentation_output_acc: 0.9507 - segmentation_output_dice_coef: 0.9329 - val_loss: 0.3087 - val_classification_output_loss: 0.5502 - val_segmentation_output_loss: 0.0671 - val_classification_output_acc: 0.6719 - val_segmentation_output_acc: 0.9510 - val_segmentation_output_dice_coef: 0.9329\n",
      "Epoch 14/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.3283 - classification_output_loss: 0.5928 - segmentation_output_loss: 0.0638 - classification_output_acc: 0.5480 - segmentation_output_acc: 0.9536 - segmentation_output_dice_coef: 0.9362 - val_loss: 0.3255 - val_classification_output_loss: 0.5614 - val_segmentation_output_loss: 0.0897 - val_classification_output_acc: 0.6719 - val_segmentation_output_acc: 0.9274 - val_segmentation_output_dice_coef: 0.9103\n",
      "Epoch 15/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.3191 - classification_output_loss: 0.5751 - segmentation_output_loss: 0.0631 - classification_output_acc: 0.6610 - segmentation_output_acc: 0.9539 - segmentation_output_dice_coef: 0.9369 - val_loss: 0.3080 - val_classification_output_loss: 0.5426 - val_segmentation_output_loss: 0.0735 - val_classification_output_acc: 0.7969 - val_segmentation_output_acc: 0.9433 - val_segmentation_output_dice_coef: 0.9265\n",
      "Epoch 16/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.3062 - classification_output_loss: 0.5513 - segmentation_output_loss: 0.0611 - classification_output_acc: 0.7540 - segmentation_output_acc: 0.9553 - segmentation_output_dice_coef: 0.9389 - val_loss: 0.3330 - val_classification_output_loss: 0.5766 - val_segmentation_output_loss: 0.0894 - val_classification_output_acc: 0.7031 - val_segmentation_output_acc: 0.9270 - val_segmentation_output_dice_coef: 0.9106\n",
      "Epoch 17/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.3013 - classification_output_loss: 0.5423 - segmentation_output_loss: 0.0603 - classification_output_acc: 0.7940 - segmentation_output_acc: 0.9558 - segmentation_output_dice_coef: 0.9397 - val_loss: 0.2837 - val_classification_output_loss: 0.5028 - val_segmentation_output_loss: 0.0647 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9513 - val_segmentation_output_dice_coef: 0.9353\n"
     ]
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     "text": [
      "Epoch 18/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2885 - classification_output_loss: 0.5182 - segmentation_output_loss: 0.0589 - classification_output_acc: 0.8420 - segmentation_output_acc: 0.9569 - segmentation_output_dice_coef: 0.9411 - val_loss: 0.2636 - val_classification_output_loss: 0.4628 - val_segmentation_output_loss: 0.0644 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9508 - val_segmentation_output_dice_coef: 0.9356\n",
      "Epoch 19/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2829 - classification_output_loss: 0.5084 - segmentation_output_loss: 0.0574 - classification_output_acc: 0.8610 - segmentation_output_acc: 0.9579 - segmentation_output_dice_coef: 0.9426 - val_loss: 0.2580 - val_classification_output_loss: 0.4515 - val_segmentation_output_loss: 0.0645 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9500 - val_segmentation_output_dice_coef: 0.9355\n",
      "Epoch 20/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2719 - classification_output_loss: 0.4877 - segmentation_output_loss: 0.0560 - classification_output_acc: 0.8960 - segmentation_output_acc: 0.9590 - segmentation_output_dice_coef: 0.9440 - val_loss: 0.2472 - val_classification_output_loss: 0.4285 - val_segmentation_output_loss: 0.0659 - val_classification_output_acc: 0.9375 - val_segmentation_output_acc: 0.9493 - val_segmentation_output_dice_coef: 0.9341\n",
      "Epoch 21/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2662 - classification_output_loss: 0.4774 - segmentation_output_loss: 0.0551 - classification_output_acc: 0.9040 - segmentation_output_acc: 0.9596 - segmentation_output_dice_coef: 0.9449 - val_loss: 0.2335 - val_classification_output_loss: 0.4048 - val_segmentation_output_loss: 0.0622 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9519 - val_segmentation_output_dice_coef: 0.9378\n",
      "Epoch 22/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2622 - classification_output_loss: 0.4699 - segmentation_output_loss: 0.0546 - classification_output_acc: 0.9170 - segmentation_output_acc: 0.9599 - segmentation_output_dice_coef: 0.9454 - val_loss: 0.2379 - val_classification_output_loss: 0.4116 - val_segmentation_output_loss: 0.0643 - val_classification_output_acc: 0.9531 - val_segmentation_output_acc: 0.9492 - val_segmentation_output_dice_coef: 0.9357\n",
      "Epoch 23/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2527 - classification_output_loss: 0.4505 - segmentation_output_loss: 0.0549 - classification_output_acc: 0.9240 - segmentation_output_acc: 0.9591 - segmentation_output_dice_coef: 0.9451 - val_loss: 0.2259 - val_classification_output_loss: 0.3909 - val_segmentation_output_loss: 0.0609 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9528 - val_segmentation_output_dice_coef: 0.9391\n",
      "Epoch 24/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2505 - classification_output_loss: 0.4480 - segmentation_output_loss: 0.0529 - classification_output_acc: 0.9450 - segmentation_output_acc: 0.9610 - segmentation_output_dice_coef: 0.9471 - val_loss: 0.2299 - val_classification_output_loss: 0.3981 - val_segmentation_output_loss: 0.0617 - val_classification_output_acc: 0.9531 - val_segmentation_output_acc: 0.9517 - val_segmentation_output_dice_coef: 0.9383\n",
      "Epoch 25/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2448 - classification_output_loss: 0.4372 - segmentation_output_loss: 0.0524 - classification_output_acc: 0.9430 - segmentation_output_acc: 0.9613 - segmentation_output_dice_coef: 0.9476 - val_loss: 0.2431 - val_classification_output_loss: 0.4232 - val_segmentation_output_loss: 0.0630 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9495 - val_segmentation_output_dice_coef: 0.9370\n",
      "Epoch 26/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2405 - classification_output_loss: 0.4293 - segmentation_output_loss: 0.0518 - classification_output_acc: 0.9490 - segmentation_output_acc: 0.9615 - segmentation_output_dice_coef: 0.9482 - val_loss: 0.2230 - val_classification_output_loss: 0.3818 - val_segmentation_output_loss: 0.0641 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9492 - val_segmentation_output_dice_coef: 0.9359\n",
      "Epoch 27/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2362 - classification_output_loss: 0.4217 - segmentation_output_loss: 0.0507 - classification_output_acc: 0.9570 - segmentation_output_acc: 0.9624 - segmentation_output_dice_coef: 0.9493 - val_loss: 0.2221 - val_classification_output_loss: 0.3753 - val_segmentation_output_loss: 0.0688 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9439 - val_segmentation_output_dice_coef: 0.9312\n",
      "Epoch 28/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2389 - classification_output_loss: 0.4270 - segmentation_output_loss: 0.0509 - classification_output_acc: 0.9550 - segmentation_output_acc: 0.9621 - segmentation_output_dice_coef: 0.9491 - val_loss: 0.2183 - val_classification_output_loss: 0.3714 - val_segmentation_output_loss: 0.0653 - val_classification_output_acc: 1.0000 - val_segmentation_output_acc: 0.9472 - val_segmentation_output_dice_coef: 0.9347\n",
      "Epoch 29/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2272 - classification_output_loss: 0.4055 - segmentation_output_loss: 0.0490 - classification_output_acc: 0.9690 - segmentation_output_acc: 0.9637 - segmentation_output_dice_coef: 0.9510 - val_loss: 0.2262 - val_classification_output_loss: 0.3867 - val_segmentation_output_loss: 0.0657 - val_classification_output_acc: 0.9531 - val_segmentation_output_acc: 0.9468 - val_segmentation_output_dice_coef: 0.9343\n",
      "Epoch 30/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2258 - classification_output_loss: 0.4025 - segmentation_output_loss: 0.0491 - classification_output_acc: 0.9750 - segmentation_output_acc: 0.9634 - segmentation_output_dice_coef: 0.9509 - val_loss: 0.2131 - val_classification_output_loss: 0.3655 - val_segmentation_output_loss: 0.0608 - val_classification_output_acc: 1.0000 - val_segmentation_output_acc: 0.9512 - val_segmentation_output_dice_coef: 0.9392\n",
      "Epoch 31/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2230 - classification_output_loss: 0.3980 - segmentation_output_loss: 0.0481 - classification_output_acc: 0.9750 - segmentation_output_acc: 0.9642 - segmentation_output_dice_coef: 0.9519 - val_loss: 0.2142 - val_classification_output_loss: 0.3669 - val_segmentation_output_loss: 0.0615 - val_classification_output_acc: 1.0000 - val_segmentation_output_acc: 0.9505 - val_segmentation_output_dice_coef: 0.9385\n",
      "Epoch 32/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2204 - classification_output_loss: 0.3934 - segmentation_output_loss: 0.0475 - classification_output_acc: 0.9800 - segmentation_output_acc: 0.9647 - segmentation_output_dice_coef: 0.9525 - val_loss: 0.2266 - val_classification_output_loss: 0.3936 - val_segmentation_output_loss: 0.0596 - val_classification_output_acc: 0.9531 - val_segmentation_output_acc: 0.9523 - val_segmentation_output_dice_coef: 0.9404\n",
      "Epoch 33/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2230 - classification_output_loss: 0.3992 - segmentation_output_loss: 0.0468 - classification_output_acc: 0.9760 - segmentation_output_acc: 0.9651 - segmentation_output_dice_coef: 0.9532 - val_loss: 0.2221 - val_classification_output_loss: 0.3700 - val_segmentation_output_loss: 0.0743 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9376 - val_segmentation_output_dice_coef: 0.9257\n",
      "Epoch 34/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2181 - classification_output_loss: 0.3901 - segmentation_output_loss: 0.0460 - classification_output_acc: 0.9780 - segmentation_output_acc: 0.9659 - segmentation_output_dice_coef: 0.9540 - val_loss: 0.2226 - val_classification_output_loss: 0.3814 - val_segmentation_output_loss: 0.0639 - val_classification_output_acc: 0.9688 - val_segmentation_output_acc: 0.9477 - val_segmentation_output_dice_coef: 0.9361\n"
     ]
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     "text": [
      "Epoch 35/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2165 - classification_output_loss: 0.3875 - segmentation_output_loss: 0.0456 - classification_output_acc: 0.9810 - segmentation_output_acc: 0.9660 - segmentation_output_dice_coef: 0.9544 - val_loss: 0.2250 - val_classification_output_loss: 0.3862 - val_segmentation_output_loss: 0.0638 - val_classification_output_acc: 0.9375 - val_segmentation_output_acc: 0.9471 - val_segmentation_output_dice_coef: 0.9362\n",
      "Epoch 36/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2125 - classification_output_loss: 0.3795 - segmentation_output_loss: 0.0455 - classification_output_acc: 0.9860 - segmentation_output_acc: 0.9661 - segmentation_output_dice_coef: 0.9545 - val_loss: 0.2076 - val_classification_output_loss: 0.3552 - val_segmentation_output_loss: 0.0600 - val_classification_output_acc: 1.0000 - val_segmentation_output_acc: 0.9510 - val_segmentation_output_dice_coef: 0.9400\n",
      "Epoch 37/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2133 - classification_output_loss: 0.3818 - segmentation_output_loss: 0.0447 - classification_output_acc: 0.9860 - segmentation_output_acc: 0.9667 - segmentation_output_dice_coef: 0.9553 - val_loss: 0.2106 - val_classification_output_loss: 0.3582 - val_segmentation_output_loss: 0.0630 - val_classification_output_acc: 1.0000 - val_segmentation_output_acc: 0.9477 - val_segmentation_output_dice_coef: 0.9370\n",
      "Epoch 38/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2148 - classification_output_loss: 0.3854 - segmentation_output_loss: 0.0442 - classification_output_acc: 0.9820 - segmentation_output_acc: 0.9671 - segmentation_output_dice_coef: 0.9558 - val_loss: 0.2214 - val_classification_output_loss: 0.3805 - val_segmentation_output_loss: 0.0624 - val_classification_output_acc: 0.9688 - val_segmentation_output_acc: 0.9488 - val_segmentation_output_dice_coef: 0.9376\n",
      "Epoch 39/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2123 - classification_output_loss: 0.3814 - segmentation_output_loss: 0.0432 - classification_output_acc: 0.9820 - segmentation_output_acc: 0.9680 - segmentation_output_dice_coef: 0.9568 - val_loss: 0.2092 - val_classification_output_loss: 0.3594 - val_segmentation_output_loss: 0.0589 - val_classification_output_acc: 1.0000 - val_segmentation_output_acc: 0.9522 - val_segmentation_output_dice_coef: 0.9411\n",
      "Epoch 40/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2094 - classification_output_loss: 0.3754 - segmentation_output_loss: 0.0434 - classification_output_acc: 0.9830 - segmentation_output_acc: 0.9678 - segmentation_output_dice_coef: 0.9566 - val_loss: 0.2115 - val_classification_output_loss: 0.3638 - val_segmentation_output_loss: 0.0592 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9513 - val_segmentation_output_dice_coef: 0.9408\n",
      "Epoch 41/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2078 - classification_output_loss: 0.3730 - segmentation_output_loss: 0.0426 - classification_output_acc: 0.9890 - segmentation_output_acc: 0.9683 - segmentation_output_dice_coef: 0.9574 - val_loss: 0.2337 - val_classification_output_loss: 0.4086 - val_segmentation_output_loss: 0.0588 - val_classification_output_acc: 0.9219 - val_segmentation_output_acc: 0.9516 - val_segmentation_output_dice_coef: 0.9412\n",
      "Epoch 42/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2079 - classification_output_loss: 0.3739 - segmentation_output_loss: 0.0418 - classification_output_acc: 0.9880 - segmentation_output_acc: 0.9691 - segmentation_output_dice_coef: 0.9582 - val_loss: 0.2073 - val_classification_output_loss: 0.3563 - val_segmentation_output_loss: 0.0583 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9520 - val_segmentation_output_dice_coef: 0.9417\n",
      "Epoch 43/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2084 - classification_output_loss: 0.3747 - segmentation_output_loss: 0.0421 - classification_output_acc: 0.9850 - segmentation_output_acc: 0.9686 - segmentation_output_dice_coef: 0.9579 - val_loss: 0.2069 - val_classification_output_loss: 0.3564 - val_segmentation_output_loss: 0.0574 - val_classification_output_acc: 1.0000 - val_segmentation_output_acc: 0.9533 - val_segmentation_output_dice_coef: 0.9426\n",
      "Epoch 44/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2040 - classification_output_loss: 0.3666 - segmentation_output_loss: 0.0413 - classification_output_acc: 0.9910 - segmentation_output_acc: 0.9693 - segmentation_output_dice_coef: 0.9587 - val_loss: 0.2142 - val_classification_output_loss: 0.3721 - val_segmentation_output_loss: 0.0563 - val_classification_output_acc: 0.9688 - val_segmentation_output_acc: 0.9539 - val_segmentation_output_dice_coef: 0.9437\n",
      "Epoch 45/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2043 - classification_output_loss: 0.3685 - segmentation_output_loss: 0.0401 - classification_output_acc: 0.9900 - segmentation_output_acc: 0.9703 - segmentation_output_dice_coef: 0.9599 - val_loss: 0.2132 - val_classification_output_loss: 0.3695 - val_segmentation_output_loss: 0.0569 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9534 - val_segmentation_output_dice_coef: 0.9431\n",
      "Epoch 46/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2052 - classification_output_loss: 0.3701 - segmentation_output_loss: 0.0403 - classification_output_acc: 0.9870 - segmentation_output_acc: 0.9701 - segmentation_output_dice_coef: 0.9597 - val_loss: 0.2091 - val_classification_output_loss: 0.3601 - val_segmentation_output_loss: 0.0581 - val_classification_output_acc: 1.0000 - val_segmentation_output_acc: 0.9519 - val_segmentation_output_dice_coef: 0.9419\n",
      "Epoch 47/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2024 - classification_output_loss: 0.3653 - segmentation_output_loss: 0.0396 - classification_output_acc: 0.9890 - segmentation_output_acc: 0.9706 - segmentation_output_dice_coef: 0.9604 - val_loss: 0.2145 - val_classification_output_loss: 0.3697 - val_segmentation_output_loss: 0.0593 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9509 - val_segmentation_output_dice_coef: 0.9407\n",
      "Epoch 48/200\n",
      "1000/1000 [==============================] - 37s 37ms/step - loss: 0.2018 - classification_output_loss: 0.3650 - segmentation_output_loss: 0.0387 - classification_output_acc: 0.9890 - segmentation_output_acc: 0.9715 - segmentation_output_dice_coef: 0.9613 - val_loss: 0.2124 - val_classification_output_loss: 0.3629 - val_segmentation_output_loss: 0.0619 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9474 - val_segmentation_output_dice_coef: 0.9381\n",
      "Epoch 49/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2006 - classification_output_loss: 0.3617 - segmentation_output_loss: 0.0396 - classification_output_acc: 0.9930 - segmentation_output_acc: 0.9705 - segmentation_output_dice_coef: 0.9604 - val_loss: 0.2052 - val_classification_output_loss: 0.3537 - val_segmentation_output_loss: 0.0567 - val_classification_output_acc: 1.0000 - val_segmentation_output_acc: 0.9530 - val_segmentation_output_dice_coef: 0.9433\n",
      "Epoch 50/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2007 - classification_output_loss: 0.3630 - segmentation_output_loss: 0.0383 - classification_output_acc: 0.9930 - segmentation_output_acc: 0.9716 - segmentation_output_dice_coef: 0.9617 - val_loss: 0.2149 - val_classification_output_loss: 0.3714 - val_segmentation_output_loss: 0.0584 - val_classification_output_acc: 0.9688 - val_segmentation_output_acc: 0.9513 - val_segmentation_output_dice_coef: 0.9416\n",
      "Epoch 51/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1992 - classification_output_loss: 0.3603 - segmentation_output_loss: 0.0380 - classification_output_acc: 0.9920 - segmentation_output_acc: 0.9718 - segmentation_output_dice_coef: 0.9620 - val_loss: 0.2046 - val_classification_output_loss: 0.3519 - val_segmentation_output_loss: 0.0573 - val_classification_output_acc: 1.0000 - val_segmentation_output_acc: 0.9525 - val_segmentation_output_dice_coef: 0.9427\n"
     ]
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     "text": [
      "Epoch 52/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1980 - classification_output_loss: 0.3586 - segmentation_output_loss: 0.0374 - classification_output_acc: 0.9930 - segmentation_output_acc: 0.9723 - segmentation_output_dice_coef: 0.9626 - val_loss: 0.2245 - val_classification_output_loss: 0.3902 - val_segmentation_output_loss: 0.0588 - val_classification_output_acc: 0.9531 - val_segmentation_output_acc: 0.9504 - val_segmentation_output_dice_coef: 0.9412\n",
      "Epoch 53/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1988 - classification_output_loss: 0.3603 - segmentation_output_loss: 0.0374 - classification_output_acc: 0.9960 - segmentation_output_acc: 0.9724 - segmentation_output_dice_coef: 0.9626 - val_loss: 0.2132 - val_classification_output_loss: 0.3667 - val_segmentation_output_loss: 0.0596 - val_classification_output_acc: 1.0000 - val_segmentation_output_acc: 0.9495 - val_segmentation_output_dice_coef: 0.9404\n",
      "Epoch 54/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.2024 - classification_output_loss: 0.3678 - segmentation_output_loss: 0.0371 - classification_output_acc: 0.9910 - segmentation_output_acc: 0.9725 - segmentation_output_dice_coef: 0.9629 - val_loss: 0.2150 - val_classification_output_loss: 0.3737 - val_segmentation_output_loss: 0.0563 - val_classification_output_acc: 0.9688 - val_segmentation_output_acc: 0.9530 - val_segmentation_output_dice_coef: 0.9437\n",
      "Epoch 55/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1969 - classification_output_loss: 0.3577 - segmentation_output_loss: 0.0361 - classification_output_acc: 0.9950 - segmentation_output_acc: 0.9734 - segmentation_output_dice_coef: 0.9639 - val_loss: 0.2066 - val_classification_output_loss: 0.3567 - val_segmentation_output_loss: 0.0565 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9529 - val_segmentation_output_dice_coef: 0.9435\n",
      "Epoch 56/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1989 - classification_output_loss: 0.3615 - segmentation_output_loss: 0.0362 - classification_output_acc: 0.9900 - segmentation_output_acc: 0.9733 - segmentation_output_dice_coef: 0.9638 - val_loss: 0.2088 - val_classification_output_loss: 0.3618 - val_segmentation_output_loss: 0.0558 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9533 - val_segmentation_output_dice_coef: 0.9442\n",
      "Epoch 57/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1946 - classification_output_loss: 0.3535 - segmentation_output_loss: 0.0357 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9735 - segmentation_output_dice_coef: 0.9643 - val_loss: 0.2061 - val_classification_output_loss: 0.3559 - val_segmentation_output_loss: 0.0562 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9528 - val_segmentation_output_dice_coef: 0.9438\n",
      "Epoch 58/200\n",
      "1000/1000 [==============================] - 37s 37ms/step - loss: 0.1986 - classification_output_loss: 0.3613 - segmentation_output_loss: 0.0358 - classification_output_acc: 0.9900 - segmentation_output_acc: 0.9734 - segmentation_output_dice_coef: 0.9642 - val_loss: 0.2078 - val_classification_output_loss: 0.3582 - val_segmentation_output_loss: 0.0573 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9520 - val_segmentation_output_dice_coef: 0.9427\n",
      "Epoch 59/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1958 - classification_output_loss: 0.3564 - segmentation_output_loss: 0.0353 - classification_output_acc: 0.9960 - segmentation_output_acc: 0.9739 - segmentation_output_dice_coef: 0.9647 - val_loss: 0.2110 - val_classification_output_loss: 0.3658 - val_segmentation_output_loss: 0.0562 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9527 - val_segmentation_output_dice_coef: 0.9438\n",
      "Epoch 60/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1965 - classification_output_loss: 0.3587 - segmentation_output_loss: 0.0343 - classification_output_acc: 0.9960 - segmentation_output_acc: 0.9747 - segmentation_output_dice_coef: 0.9657 - val_loss: 0.2176 - val_classification_output_loss: 0.3793 - val_segmentation_output_loss: 0.0560 - val_classification_output_acc: 0.9688 - val_segmentation_output_acc: 0.9528 - val_segmentation_output_dice_coef: 0.9440\n",
      "Epoch 61/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1981 - classification_output_loss: 0.3615 - segmentation_output_loss: 0.0347 - classification_output_acc: 0.9960 - segmentation_output_acc: 0.9744 - segmentation_output_dice_coef: 0.9653 - val_loss: 0.2128 - val_classification_output_loss: 0.3555 - val_segmentation_output_loss: 0.0701 - val_classification_output_acc: 1.0000 - val_segmentation_output_acc: 0.9385 - val_segmentation_output_dice_coef: 0.9299\n",
      "Epoch 62/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1965 - classification_output_loss: 0.3589 - segmentation_output_loss: 0.0342 - classification_output_acc: 0.9950 - segmentation_output_acc: 0.9748 - segmentation_output_dice_coef: 0.9658 - val_loss: 0.2184 - val_classification_output_loss: 0.3801 - val_segmentation_output_loss: 0.0568 - val_classification_output_acc: 0.9688 - val_segmentation_output_acc: 0.9521 - val_segmentation_output_dice_coef: 0.9432\n",
      "Epoch 63/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1942 - classification_output_loss: 0.3547 - segmentation_output_loss: 0.0337 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9753 - segmentation_output_dice_coef: 0.9663 - val_loss: 0.2224 - val_classification_output_loss: 0.3848 - val_segmentation_output_loss: 0.0599 - val_classification_output_acc: 0.9688 - val_segmentation_output_acc: 0.9487 - val_segmentation_output_dice_coef: 0.9401\n",
      "Epoch 64/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1990 - classification_output_loss: 0.3638 - segmentation_output_loss: 0.0343 - classification_output_acc: 0.9930 - segmentation_output_acc: 0.9746 - segmentation_output_dice_coef: 0.9657 - val_loss: 0.2052 - val_classification_output_loss: 0.3546 - val_segmentation_output_loss: 0.0557 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9529 - val_segmentation_output_dice_coef: 0.9443\n",
      "Epoch 65/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1938 - classification_output_loss: 0.3540 - segmentation_output_loss: 0.0335 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9753 - segmentation_output_dice_coef: 0.9665 - val_loss: 0.2049 - val_classification_output_loss: 0.3543 - val_segmentation_output_loss: 0.0555 - val_classification_output_acc: 1.0000 - val_segmentation_output_acc: 0.9529 - val_segmentation_output_dice_coef: 0.9445\n",
      "Epoch 66/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1929 - classification_output_loss: 0.3531 - segmentation_output_loss: 0.0327 - classification_output_acc: 0.9960 - segmentation_output_acc: 0.9761 - segmentation_output_dice_coef: 0.9673 - val_loss: 0.2174 - val_classification_output_loss: 0.3793 - val_segmentation_output_loss: 0.0554 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9530 - val_segmentation_output_dice_coef: 0.9446\n",
      "Epoch 67/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1957 - classification_output_loss: 0.3584 - segmentation_output_loss: 0.0331 - classification_output_acc: 0.9940 - segmentation_output_acc: 0.9757 - segmentation_output_dice_coef: 0.9669 - val_loss: 0.2187 - val_classification_output_loss: 0.3773 - val_segmentation_output_loss: 0.0602 - val_classification_output_acc: 0.9688 - val_segmentation_output_acc: 0.9480 - val_segmentation_output_dice_coef: 0.9398\n",
      "Epoch 68/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1947 - classification_output_loss: 0.3571 - segmentation_output_loss: 0.0323 - classification_output_acc: 0.9940 - segmentation_output_acc: 0.9763 - segmentation_output_dice_coef: 0.9677 - val_loss: 0.2088 - val_classification_output_loss: 0.3566 - val_segmentation_output_loss: 0.0610 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9471 - val_segmentation_output_dice_coef: 0.9390\n"
     ]
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     "text": [
      "Epoch 69/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1951 - classification_output_loss: 0.3575 - segmentation_output_loss: 0.0327 - classification_output_acc: 0.9920 - segmentation_output_acc: 0.9758 - segmentation_output_dice_coef: 0.9673 - val_loss: 0.2087 - val_classification_output_loss: 0.3602 - val_segmentation_output_loss: 0.0571 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9511 - val_segmentation_output_dice_coef: 0.9429\n",
      "Epoch 70/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1936 - classification_output_loss: 0.3549 - segmentation_output_loss: 0.0323 - classification_output_acc: 0.9960 - segmentation_output_acc: 0.9762 - segmentation_output_dice_coef: 0.9677 - val_loss: 0.2069 - val_classification_output_loss: 0.3567 - val_segmentation_output_loss: 0.0571 - val_classification_output_acc: 1.0000 - val_segmentation_output_acc: 0.9508 - val_segmentation_output_dice_coef: 0.9429\n",
      "Epoch 71/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1933 - classification_output_loss: 0.3547 - segmentation_output_loss: 0.0319 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9765 - segmentation_output_dice_coef: 0.9681 - val_loss: 0.2092 - val_classification_output_loss: 0.3612 - val_segmentation_output_loss: 0.0573 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9510 - val_segmentation_output_dice_coef: 0.9427\n",
      "Epoch 72/200\n",
      "1000/1000 [==============================] - 37s 37ms/step - loss: 0.1951 - classification_output_loss: 0.3584 - segmentation_output_loss: 0.0318 - classification_output_acc: 0.9920 - segmentation_output_acc: 0.9766 - segmentation_output_dice_coef: 0.9682 - val_loss: 0.2202 - val_classification_output_loss: 0.3742 - val_segmentation_output_loss: 0.0662 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9419 - val_segmentation_output_dice_coef: 0.9338\n",
      "Epoch 73/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1936 - classification_output_loss: 0.3563 - segmentation_output_loss: 0.0309 - classification_output_acc: 0.9930 - segmentation_output_acc: 0.9773 - segmentation_output_dice_coef: 0.9691 - val_loss: 0.2140 - val_classification_output_loss: 0.3712 - val_segmentation_output_loss: 0.0568 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9513 - val_segmentation_output_dice_coef: 0.9432\n",
      "Epoch 74/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1919 - classification_output_loss: 0.3527 - segmentation_output_loss: 0.0311 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9772 - segmentation_output_dice_coef: 0.9689 - val_loss: 0.2216 - val_classification_output_loss: 0.3870 - val_segmentation_output_loss: 0.0561 - val_classification_output_acc: 0.9531 - val_segmentation_output_acc: 0.9516 - val_segmentation_output_dice_coef: 0.9439\n",
      "Epoch 75/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1927 - classification_output_loss: 0.3547 - segmentation_output_loss: 0.0308 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9774 - segmentation_output_dice_coef: 0.9692 - val_loss: 0.2110 - val_classification_output_loss: 0.3593 - val_segmentation_output_loss: 0.0626 - val_classification_output_acc: 1.0000 - val_segmentation_output_acc: 0.9452 - val_segmentation_output_dice_coef: 0.9374\n",
      "Epoch 76/200\n",
      "1000/1000 [==============================] - 37s 37ms/step - loss: 0.1924 - classification_output_loss: 0.3540 - segmentation_output_loss: 0.0308 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9774 - segmentation_output_dice_coef: 0.9692 - val_loss: 0.2135 - val_classification_output_loss: 0.3687 - val_segmentation_output_loss: 0.0584 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9494 - val_segmentation_output_dice_coef: 0.9416\n",
      "Epoch 77/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1927 - classification_output_loss: 0.3551 - segmentation_output_loss: 0.0303 - classification_output_acc: 0.9920 - segmentation_output_acc: 0.9778 - segmentation_output_dice_coef: 0.9697 - val_loss: 0.2190 - val_classification_output_loss: 0.3817 - val_segmentation_output_loss: 0.0562 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9516 - val_segmentation_output_dice_coef: 0.9438\n",
      "Epoch 78/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1910 - classification_output_loss: 0.3523 - segmentation_output_loss: 0.0298 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9783 - segmentation_output_dice_coef: 0.9702 - val_loss: 0.2130 - val_classification_output_loss: 0.3703 - val_segmentation_output_loss: 0.0557 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9521 - val_segmentation_output_dice_coef: 0.9443\n",
      "Epoch 79/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1943 - classification_output_loss: 0.3585 - segmentation_output_loss: 0.0301 - classification_output_acc: 0.9960 - segmentation_output_acc: 0.9779 - segmentation_output_dice_coef: 0.9699 - val_loss: 0.2163 - val_classification_output_loss: 0.3747 - val_segmentation_output_loss: 0.0578 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9498 - val_segmentation_output_dice_coef: 0.9422\n",
      "Epoch 80/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1919 - classification_output_loss: 0.3541 - segmentation_output_loss: 0.0296 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9784 - segmentation_output_dice_coef: 0.9704 - val_loss: 0.2054 - val_classification_output_loss: 0.3549 - val_segmentation_output_loss: 0.0558 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9518 - val_segmentation_output_dice_coef: 0.9442\n",
      "Epoch 81/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1920 - classification_output_loss: 0.3549 - segmentation_output_loss: 0.0291 - classification_output_acc: 0.9960 - segmentation_output_acc: 0.9787 - segmentation_output_dice_coef: 0.9709 - val_loss: 0.2082 - val_classification_output_loss: 0.3606 - val_segmentation_output_loss: 0.0557 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9519 - val_segmentation_output_dice_coef: 0.9443\n",
      "Epoch 82/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1945 - classification_output_loss: 0.3601 - segmentation_output_loss: 0.0290 - classification_output_acc: 0.9920 - segmentation_output_acc: 0.9788 - segmentation_output_dice_coef: 0.9710 - val_loss: 0.2143 - val_classification_output_loss: 0.3688 - val_segmentation_output_loss: 0.0599 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9478 - val_segmentation_output_dice_coef: 0.9401\n",
      "Epoch 83/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1910 - classification_output_loss: 0.3534 - segmentation_output_loss: 0.0286 - classification_output_acc: 0.9930 - segmentation_output_acc: 0.9792 - segmentation_output_dice_coef: 0.9714 - val_loss: 0.2154 - val_classification_output_loss: 0.3695 - val_segmentation_output_loss: 0.0614 - val_classification_output_acc: 0.9688 - val_segmentation_output_acc: 0.9463 - val_segmentation_output_dice_coef: 0.9386\n",
      "Epoch 84/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1924 - classification_output_loss: 0.3558 - segmentation_output_loss: 0.0289 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9788 - segmentation_output_dice_coef: 0.9711 - val_loss: 0.2122 - val_classification_output_loss: 0.3681 - val_segmentation_output_loss: 0.0563 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9511 - val_segmentation_output_dice_coef: 0.9437\n",
      "Epoch 85/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1910 - classification_output_loss: 0.3528 - segmentation_output_loss: 0.0291 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9786 - segmentation_output_dice_coef: 0.9709 - val_loss: 0.2083 - val_classification_output_loss: 0.3616 - val_segmentation_output_loss: 0.0550 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9526 - val_segmentation_output_dice_coef: 0.9450\n"
     ]
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     "text": [
      "Epoch 86/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1908 - classification_output_loss: 0.3529 - segmentation_output_loss: 0.0287 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9790 - segmentation_output_dice_coef: 0.9713 - val_loss: 0.2048 - val_classification_output_loss: 0.3512 - val_segmentation_output_loss: 0.0585 - val_classification_output_acc: 1.0000 - val_segmentation_output_acc: 0.9486 - val_segmentation_output_dice_coef: 0.9415\n",
      "Epoch 87/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1902 - classification_output_loss: 0.3518 - segmentation_output_loss: 0.0286 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9791 - segmentation_output_dice_coef: 0.9714 - val_loss: 0.2111 - val_classification_output_loss: 0.3652 - val_segmentation_output_loss: 0.0569 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9504 - val_segmentation_output_dice_coef: 0.9431\n",
      "Epoch 88/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1925 - classification_output_loss: 0.3566 - segmentation_output_loss: 0.0283 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9793 - segmentation_output_dice_coef: 0.9717 - val_loss: 0.2088 - val_classification_output_loss: 0.3608 - val_segmentation_output_loss: 0.0569 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9505 - val_segmentation_output_dice_coef: 0.9431\n",
      "Epoch 89/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1906 - classification_output_loss: 0.3532 - segmentation_output_loss: 0.0280 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9796 - segmentation_output_dice_coef: 0.9720 - val_loss: 0.2100 - val_classification_output_loss: 0.3608 - val_segmentation_output_loss: 0.0592 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9479 - val_segmentation_output_dice_coef: 0.9408\n",
      "Epoch 90/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1917 - classification_output_loss: 0.3556 - segmentation_output_loss: 0.0277 - classification_output_acc: 0.9950 - segmentation_output_acc: 0.9798 - segmentation_output_dice_coef: 0.9723 - val_loss: 0.2115 - val_classification_output_loss: 0.3668 - val_segmentation_output_loss: 0.0563 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9508 - val_segmentation_output_dice_coef: 0.9437\n",
      "Epoch 91/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1905 - classification_output_loss: 0.3538 - segmentation_output_loss: 0.0271 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9802 - segmentation_output_dice_coef: 0.9729 - val_loss: 0.2151 - val_classification_output_loss: 0.3737 - val_segmentation_output_loss: 0.0565 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9509 - val_segmentation_output_dice_coef: 0.9435\n",
      "Epoch 92/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1898 - classification_output_loss: 0.3523 - segmentation_output_loss: 0.0272 - classification_output_acc: 0.9950 - segmentation_output_acc: 0.9802 - segmentation_output_dice_coef: 0.9728 - val_loss: 0.2225 - val_classification_output_loss: 0.3902 - val_segmentation_output_loss: 0.0549 - val_classification_output_acc: 0.9688 - val_segmentation_output_acc: 0.9524 - val_segmentation_output_dice_coef: 0.9451\n",
      "Epoch 93/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1905 - classification_output_loss: 0.3542 - segmentation_output_loss: 0.0269 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9804 - segmentation_output_dice_coef: 0.9731 - val_loss: 0.2142 - val_classification_output_loss: 0.3688 - val_segmentation_output_loss: 0.0596 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9475 - val_segmentation_output_dice_coef: 0.9404\n",
      "Epoch 94/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1904 - classification_output_loss: 0.3536 - segmentation_output_loss: 0.0272 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9801 - segmentation_output_dice_coef: 0.9728 - val_loss: 0.2134 - val_classification_output_loss: 0.3717 - val_segmentation_output_loss: 0.0552 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9519 - val_segmentation_output_dice_coef: 0.9448\n",
      "Epoch 95/200\n",
      "1000/1000 [==============================] - 37s 37ms/step - loss: 0.1911 - classification_output_loss: 0.3556 - segmentation_output_loss: 0.0266 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9808 - segmentation_output_dice_coef: 0.9734 - val_loss: 0.2113 - val_classification_output_loss: 0.3669 - val_segmentation_output_loss: 0.0557 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9514 - val_segmentation_output_dice_coef: 0.9443\n",
      "Epoch 96/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1888 - classification_output_loss: 0.3508 - segmentation_output_loss: 0.0269 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9805 - segmentation_output_dice_coef: 0.9731 - val_loss: 0.2065 - val_classification_output_loss: 0.3572 - val_segmentation_output_loss: 0.0557 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9514 - val_segmentation_output_dice_coef: 0.9443\n",
      "Epoch 97/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1899 - classification_output_loss: 0.3534 - segmentation_output_loss: 0.0265 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9807 - segmentation_output_dice_coef: 0.9735 - val_loss: 0.2112 - val_classification_output_loss: 0.3665 - val_segmentation_output_loss: 0.0559 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9511 - val_segmentation_output_dice_coef: 0.9441\n",
      "Epoch 98/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1915 - classification_output_loss: 0.3566 - segmentation_output_loss: 0.0264 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9808 - segmentation_output_dice_coef: 0.9736 - val_loss: 0.2110 - val_classification_output_loss: 0.3630 - val_segmentation_output_loss: 0.0589 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9480 - val_segmentation_output_dice_coef: 0.9411\n",
      "Epoch 99/200\n",
      "1000/1000 [==============================] - 37s 37ms/step - loss: 0.1889 - classification_output_loss: 0.3517 - segmentation_output_loss: 0.0260 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9811 - segmentation_output_dice_coef: 0.9740 - val_loss: 0.2209 - val_classification_output_loss: 0.3862 - val_segmentation_output_loss: 0.0556 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9512 - val_segmentation_output_dice_coef: 0.9444\n",
      "Epoch 100/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1895 - classification_output_loss: 0.3532 - segmentation_output_loss: 0.0259 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9813 - segmentation_output_dice_coef: 0.9741 - val_loss: 0.2178 - val_classification_output_loss: 0.3799 - val_segmentation_output_loss: 0.0556 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9513 - val_segmentation_output_dice_coef: 0.9444\n",
      "Epoch 101/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1880 - classification_output_loss: 0.3506 - segmentation_output_loss: 0.0255 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9816 - segmentation_output_dice_coef: 0.9745 - val_loss: 0.2186 - val_classification_output_loss: 0.3810 - val_segmentation_output_loss: 0.0561 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9507 - val_segmentation_output_dice_coef: 0.9439\n",
      "Epoch 102/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1893 - classification_output_loss: 0.3531 - segmentation_output_loss: 0.0255 - classification_output_acc: 0.9960 - segmentation_output_acc: 0.9816 - segmentation_output_dice_coef: 0.9745 - val_loss: 0.2140 - val_classification_output_loss: 0.3706 - val_segmentation_output_loss: 0.0575 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9493 - val_segmentation_output_dice_coef: 0.9425\n"
     ]
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     "text": [
      "Epoch 103/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1906 - classification_output_loss: 0.3557 - segmentation_output_loss: 0.0255 - classification_output_acc: 0.9960 - segmentation_output_acc: 0.9816 - segmentation_output_dice_coef: 0.9745 - val_loss: 0.2082 - val_classification_output_loss: 0.3584 - val_segmentation_output_loss: 0.0580 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9487 - val_segmentation_output_dice_coef: 0.9420\n",
      "Epoch 104/200\n",
      "1000/1000 [==============================] - 37s 37ms/step - loss: 0.1887 - classification_output_loss: 0.3523 - segmentation_output_loss: 0.0251 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9819 - segmentation_output_dice_coef: 0.9749 - val_loss: 0.2134 - val_classification_output_loss: 0.3728 - val_segmentation_output_loss: 0.0541 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9526 - val_segmentation_output_dice_coef: 0.9459\n",
      "Epoch 105/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1899 - classification_output_loss: 0.3544 - segmentation_output_loss: 0.0254 - classification_output_acc: 0.9940 - segmentation_output_acc: 0.9816 - segmentation_output_dice_coef: 0.9746 - val_loss: 0.2139 - val_classification_output_loss: 0.3689 - val_segmentation_output_loss: 0.0590 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9477 - val_segmentation_output_dice_coef: 0.9410\n",
      "Epoch 106/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1879 - classification_output_loss: 0.3507 - segmentation_output_loss: 0.0251 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9820 - segmentation_output_dice_coef: 0.9749 - val_loss: 0.2182 - val_classification_output_loss: 0.3764 - val_segmentation_output_loss: 0.0601 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9465 - val_segmentation_output_dice_coef: 0.9399\n",
      "Epoch 107/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1882 - classification_output_loss: 0.3514 - segmentation_output_loss: 0.0250 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9819 - segmentation_output_dice_coef: 0.9750 - val_loss: 0.2102 - val_classification_output_loss: 0.3642 - val_segmentation_output_loss: 0.0562 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9505 - val_segmentation_output_dice_coef: 0.9438\n",
      "Epoch 108/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1877 - classification_output_loss: 0.3505 - segmentation_output_loss: 0.0249 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9820 - segmentation_output_dice_coef: 0.9751 - val_loss: 0.2111 - val_classification_output_loss: 0.3662 - val_segmentation_output_loss: 0.0561 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9504 - val_segmentation_output_dice_coef: 0.9439\n",
      "Epoch 109/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1903 - classification_output_loss: 0.3555 - segmentation_output_loss: 0.0251 - classification_output_acc: 0.9950 - segmentation_output_acc: 0.9817 - segmentation_output_dice_coef: 0.9749 - val_loss: 0.2183 - val_classification_output_loss: 0.3809 - val_segmentation_output_loss: 0.0558 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9506 - val_segmentation_output_dice_coef: 0.9442\n",
      "Epoch 110/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1871 - classification_output_loss: 0.3500 - segmentation_output_loss: 0.0243 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9824 - segmentation_output_dice_coef: 0.9757 - val_loss: 0.2125 - val_classification_output_loss: 0.3694 - val_segmentation_output_loss: 0.0555 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9510 - val_segmentation_output_dice_coef: 0.9445\n",
      "Epoch 111/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1893 - classification_output_loss: 0.3545 - segmentation_output_loss: 0.0242 - classification_output_acc: 0.9960 - segmentation_output_acc: 0.9825 - segmentation_output_dice_coef: 0.9758 - val_loss: 0.2115 - val_classification_output_loss: 0.3694 - val_segmentation_output_loss: 0.0535 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9531 - val_segmentation_output_dice_coef: 0.9465\n",
      "Epoch 112/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1874 - classification_output_loss: 0.3504 - segmentation_output_loss: 0.0244 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9824 - segmentation_output_dice_coef: 0.9756 - val_loss: 0.2103 - val_classification_output_loss: 0.3632 - val_segmentation_output_loss: 0.0575 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9491 - val_segmentation_output_dice_coef: 0.9425\n",
      "Epoch 113/200\n",
      "1000/1000 [==============================] - 37s 37ms/step - loss: 0.1881 - classification_output_loss: 0.3522 - segmentation_output_loss: 0.0241 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9827 - segmentation_output_dice_coef: 0.9759 - val_loss: 0.2129 - val_classification_output_loss: 0.3695 - val_segmentation_output_loss: 0.0562 - val_classification_output_acc: 0.9688 - val_segmentation_output_acc: 0.9501 - val_segmentation_output_dice_coef: 0.9438\n",
      "Epoch 114/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1892 - classification_output_loss: 0.3543 - segmentation_output_loss: 0.0241 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9825 - segmentation_output_dice_coef: 0.9759 - val_loss: 0.2129 - val_classification_output_loss: 0.3686 - val_segmentation_output_loss: 0.0571 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9493 - val_segmentation_output_dice_coef: 0.9429\n",
      "Epoch 115/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1894 - classification_output_loss: 0.3552 - segmentation_output_loss: 0.0236 - classification_output_acc: 0.9950 - segmentation_output_acc: 0.9830 - segmentation_output_dice_coef: 0.9764 - val_loss: 0.2139 - val_classification_output_loss: 0.3650 - val_segmentation_output_loss: 0.0627 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9436 - val_segmentation_output_dice_coef: 0.9373\n",
      "Epoch 116/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1881 - classification_output_loss: 0.3526 - segmentation_output_loss: 0.0235 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9831 - segmentation_output_dice_coef: 0.9765 - val_loss: 0.2114 - val_classification_output_loss: 0.3660 - val_segmentation_output_loss: 0.0568 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9494 - val_segmentation_output_dice_coef: 0.9432\n",
      "Epoch 117/200\n",
      "1000/1000 [==============================] - 37s 37ms/step - loss: 0.1890 - classification_output_loss: 0.3543 - segmentation_output_loss: 0.0236 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9830 - segmentation_output_dice_coef: 0.9764 - val_loss: 0.2092 - val_classification_output_loss: 0.3627 - val_segmentation_output_loss: 0.0556 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9506 - val_segmentation_output_dice_coef: 0.9444\n",
      "Epoch 118/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1870 - classification_output_loss: 0.3508 - segmentation_output_loss: 0.0233 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9833 - segmentation_output_dice_coef: 0.9767 - val_loss: 0.2146 - val_classification_output_loss: 0.3725 - val_segmentation_output_loss: 0.0567 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9495 - val_segmentation_output_dice_coef: 0.9433\n",
      "Epoch 119/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1879 - classification_output_loss: 0.3526 - segmentation_output_loss: 0.0232 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9833 - segmentation_output_dice_coef: 0.9768 - val_loss: 0.2127 - val_classification_output_loss: 0.3666 - val_segmentation_output_loss: 0.0587 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9475 - val_segmentation_output_dice_coef: 0.9413\n"
     ]
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     "text": [
      "Epoch 120/200\n",
      "1000/1000 [==============================] - 37s 37ms/step - loss: 0.1863 - classification_output_loss: 0.3496 - segmentation_output_loss: 0.0230 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9835 - segmentation_output_dice_coef: 0.9770 - val_loss: 0.2145 - val_classification_output_loss: 0.3717 - val_segmentation_output_loss: 0.0573 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9489 - val_segmentation_output_dice_coef: 0.9427\n",
      "Epoch 121/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1882 - classification_output_loss: 0.3535 - segmentation_output_loss: 0.0228 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9836 - segmentation_output_dice_coef: 0.9772 - val_loss: 0.2138 - val_classification_output_loss: 0.3724 - val_segmentation_output_loss: 0.0552 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9511 - val_segmentation_output_dice_coef: 0.9448\n",
      "Epoch 122/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1857 - classification_output_loss: 0.3486 - segmentation_output_loss: 0.0228 - classification_output_acc: 1.0000 - segmentation_output_acc: 0.9836 - segmentation_output_dice_coef: 0.9772 - val_loss: 0.2197 - val_classification_output_loss: 0.3849 - val_segmentation_output_loss: 0.0545 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9517 - val_segmentation_output_dice_coef: 0.9455\n",
      "Epoch 123/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1871 - classification_output_loss: 0.3514 - segmentation_output_loss: 0.0229 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9835 - segmentation_output_dice_coef: 0.9771 - val_loss: 0.2156 - val_classification_output_loss: 0.3743 - val_segmentation_output_loss: 0.0568 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9493 - val_segmentation_output_dice_coef: 0.9432\n",
      "Epoch 124/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1876 - classification_output_loss: 0.3525 - segmentation_output_loss: 0.0227 - classification_output_acc: 0.9960 - segmentation_output_acc: 0.9836 - segmentation_output_dice_coef: 0.9773 - val_loss: 0.2166 - val_classification_output_loss: 0.3785 - val_segmentation_output_loss: 0.0547 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9514 - val_segmentation_output_dice_coef: 0.9453\n",
      "Epoch 125/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1865 - classification_output_loss: 0.3504 - segmentation_output_loss: 0.0226 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9837 - segmentation_output_dice_coef: 0.9774 - val_loss: 0.2123 - val_classification_output_loss: 0.3679 - val_segmentation_output_loss: 0.0567 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9492 - val_segmentation_output_dice_coef: 0.9433\n",
      "Epoch 126/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1866 - classification_output_loss: 0.3506 - segmentation_output_loss: 0.0225 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9838 - segmentation_output_dice_coef: 0.9775 - val_loss: 0.2141 - val_classification_output_loss: 0.3728 - val_segmentation_output_loss: 0.0553 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9507 - val_segmentation_output_dice_coef: 0.9447\n",
      "Epoch 127/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1869 - classification_output_loss: 0.3514 - segmentation_output_loss: 0.0223 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9840 - segmentation_output_dice_coef: 0.9777 - val_loss: 0.2170 - val_classification_output_loss: 0.3769 - val_segmentation_output_loss: 0.0571 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9489 - val_segmentation_output_dice_coef: 0.9429\n",
      "Epoch 128/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1873 - classification_output_loss: 0.3524 - segmentation_output_loss: 0.0223 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9840 - segmentation_output_dice_coef: 0.9777 - val_loss: 0.2172 - val_classification_output_loss: 0.3783 - val_segmentation_output_loss: 0.0560 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9500 - val_segmentation_output_dice_coef: 0.9440\n",
      "Epoch 129/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1873 - classification_output_loss: 0.3528 - segmentation_output_loss: 0.0218 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9843 - segmentation_output_dice_coef: 0.9782 - val_loss: 0.2084 - val_classification_output_loss: 0.3602 - val_segmentation_output_loss: 0.0566 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9494 - val_segmentation_output_dice_coef: 0.9434\n",
      "Epoch 130/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1855 - classification_output_loss: 0.3488 - segmentation_output_loss: 0.0223 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9840 - segmentation_output_dice_coef: 0.9777 - val_loss: 0.2114 - val_classification_output_loss: 0.3672 - val_segmentation_output_loss: 0.0555 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9503 - val_segmentation_output_dice_coef: 0.9445\n",
      "Epoch 131/200\n",
      "1000/1000 [==============================] - 37s 37ms/step - loss: 0.1868 - classification_output_loss: 0.3514 - segmentation_output_loss: 0.0222 - classification_output_acc: 0.9960 - segmentation_output_acc: 0.9840 - segmentation_output_dice_coef: 0.9778 - val_loss: 0.2147 - val_classification_output_loss: 0.3730 - val_segmentation_output_loss: 0.0564 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9495 - val_segmentation_output_dice_coef: 0.9436\n",
      "Epoch 132/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1866 - classification_output_loss: 0.3514 - segmentation_output_loss: 0.0217 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9844 - segmentation_output_dice_coef: 0.9783 - val_loss: 0.2112 - val_classification_output_loss: 0.3641 - val_segmentation_output_loss: 0.0583 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9475 - val_segmentation_output_dice_coef: 0.9417\n",
      "Epoch 133/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1895 - classification_output_loss: 0.3572 - segmentation_output_loss: 0.0218 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9843 - segmentation_output_dice_coef: 0.9782 - val_loss: 0.2177 - val_classification_output_loss: 0.3756 - val_segmentation_output_loss: 0.0599 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9458 - val_segmentation_output_dice_coef: 0.9401\n",
      "Epoch 134/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1875 - classification_output_loss: 0.3530 - segmentation_output_loss: 0.0219 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9842 - segmentation_output_dice_coef: 0.9781 - val_loss: 0.2183 - val_classification_output_loss: 0.3800 - val_segmentation_output_loss: 0.0566 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9494 - val_segmentation_output_dice_coef: 0.9434\n",
      "Epoch 135/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1862 - classification_output_loss: 0.3508 - segmentation_output_loss: 0.0215 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9846 - segmentation_output_dice_coef: 0.9785 - val_loss: 0.2099 - val_classification_output_loss: 0.3630 - val_segmentation_output_loss: 0.0568 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9490 - val_segmentation_output_dice_coef: 0.9432\n",
      "Epoch 136/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1860 - classification_output_loss: 0.3508 - segmentation_output_loss: 0.0212 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9847 - segmentation_output_dice_coef: 0.9788 - val_loss: 0.2075 - val_classification_output_loss: 0.3584 - val_segmentation_output_loss: 0.0566 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9492 - val_segmentation_output_dice_coef: 0.9434\n"
     ]
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     "text": [
      "Epoch 137/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1858 - classification_output_loss: 0.3505 - segmentation_output_loss: 0.0210 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9849 - segmentation_output_dice_coef: 0.9790 - val_loss: 0.2142 - val_classification_output_loss: 0.3708 - val_segmentation_output_loss: 0.0575 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9482 - val_segmentation_output_dice_coef: 0.9425\n",
      "Epoch 138/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1872 - classification_output_loss: 0.3527 - segmentation_output_loss: 0.0218 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9843 - segmentation_output_dice_coef: 0.9782 - val_loss: 0.2132 - val_classification_output_loss: 0.3697 - val_segmentation_output_loss: 0.0567 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9489 - val_segmentation_output_dice_coef: 0.9433\n",
      "Epoch 139/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1876 - classification_output_loss: 0.3537 - segmentation_output_loss: 0.0216 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9844 - segmentation_output_dice_coef: 0.9784 - val_loss: 0.2105 - val_classification_output_loss: 0.3647 - val_segmentation_output_loss: 0.0563 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9494 - val_segmentation_output_dice_coef: 0.9437\n",
      "Epoch 140/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1858 - classification_output_loss: 0.3506 - segmentation_output_loss: 0.0209 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9850 - segmentation_output_dice_coef: 0.9791 - val_loss: 0.2136 - val_classification_output_loss: 0.3714 - val_segmentation_output_loss: 0.0558 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9499 - val_segmentation_output_dice_coef: 0.9442\n",
      "Epoch 141/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1854 - classification_output_loss: 0.3496 - segmentation_output_loss: 0.0212 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9848 - segmentation_output_dice_coef: 0.9788 - val_loss: 0.2128 - val_classification_output_loss: 0.3700 - val_segmentation_output_loss: 0.0557 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9500 - val_segmentation_output_dice_coef: 0.9443\n",
      "Epoch 142/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1864 - classification_output_loss: 0.3520 - segmentation_output_loss: 0.0208 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9851 - segmentation_output_dice_coef: 0.9792 - val_loss: 0.2228 - val_classification_output_loss: 0.3892 - val_segmentation_output_loss: 0.0565 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9492 - val_segmentation_output_dice_coef: 0.9435\n",
      "\n",
      "Epoch 00142: ReduceLROnPlateau reducing learning rate to 3.9999998989515007e-05.\n",
      "Epoch 143/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1857 - classification_output_loss: 0.3508 - segmentation_output_loss: 0.0205 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9853 - segmentation_output_dice_coef: 0.9795 - val_loss: 0.2159 - val_classification_output_loss: 0.3761 - val_segmentation_output_loss: 0.0558 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9498 - val_segmentation_output_dice_coef: 0.9442\n",
      "Epoch 144/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1865 - classification_output_loss: 0.3524 - segmentation_output_loss: 0.0205 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9854 - segmentation_output_dice_coef: 0.9795 - val_loss: 0.2155 - val_classification_output_loss: 0.3745 - val_segmentation_output_loss: 0.0566 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9490 - val_segmentation_output_dice_coef: 0.9434\n",
      "Epoch 145/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1856 - classification_output_loss: 0.3508 - segmentation_output_loss: 0.0204 - classification_output_acc: 1.0000 - segmentation_output_acc: 0.9855 - segmentation_output_dice_coef: 0.9796 - val_loss: 0.2129 - val_classification_output_loss: 0.3698 - val_segmentation_output_loss: 0.0560 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9496 - val_segmentation_output_dice_coef: 0.9440\n",
      "Epoch 146/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1859 - classification_output_loss: 0.3514 - segmentation_output_loss: 0.0204 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9854 - segmentation_output_dice_coef: 0.9796 - val_loss: 0.2146 - val_classification_output_loss: 0.3723 - val_segmentation_output_loss: 0.0569 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9487 - val_segmentation_output_dice_coef: 0.9431\n",
      "Epoch 147/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1856 - classification_output_loss: 0.3508 - segmentation_output_loss: 0.0204 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9854 - segmentation_output_dice_coef: 0.9796 - val_loss: 0.2158 - val_classification_output_loss: 0.3752 - val_segmentation_output_loss: 0.0565 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9491 - val_segmentation_output_dice_coef: 0.9435\n",
      "Epoch 148/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1850 - classification_output_loss: 0.3495 - segmentation_output_loss: 0.0205 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9854 - segmentation_output_dice_coef: 0.9795 - val_loss: 0.2171 - val_classification_output_loss: 0.3779 - val_segmentation_output_loss: 0.0563 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9493 - val_segmentation_output_dice_coef: 0.9437\n",
      "Epoch 149/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1853 - classification_output_loss: 0.3503 - segmentation_output_loss: 0.0203 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9856 - segmentation_output_dice_coef: 0.9797 - val_loss: 0.2154 - val_classification_output_loss: 0.3737 - val_segmentation_output_loss: 0.0570 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9487 - val_segmentation_output_dice_coef: 0.9430\n",
      "Epoch 150/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1864 - classification_output_loss: 0.3522 - segmentation_output_loss: 0.0205 - classification_output_acc: 1.0000 - segmentation_output_acc: 0.9854 - segmentation_output_dice_coef: 0.9795 - val_loss: 0.2162 - val_classification_output_loss: 0.3761 - val_segmentation_output_loss: 0.0564 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9492 - val_segmentation_output_dice_coef: 0.9436\n",
      "Epoch 151/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1851 - classification_output_loss: 0.3497 - segmentation_output_loss: 0.0205 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9854 - segmentation_output_dice_coef: 0.9795 - val_loss: 0.2137 - val_classification_output_loss: 0.3709 - val_segmentation_output_loss: 0.0565 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9490 - val_segmentation_output_dice_coef: 0.9435\n",
      "Epoch 152/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1848 - classification_output_loss: 0.3496 - segmentation_output_loss: 0.0200 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9858 - segmentation_output_dice_coef: 0.9800 - val_loss: 0.2145 - val_classification_output_loss: 0.3726 - val_segmentation_output_loss: 0.0564 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9492 - val_segmentation_output_dice_coef: 0.9436\n",
      "Epoch 153/200\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1863 - classification_output_loss: 0.3524 - segmentation_output_loss: 0.0201 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9856 - segmentation_output_dice_coef: 0.9799 - val_loss: 0.2152 - val_classification_output_loss: 0.3741 - val_segmentation_output_loss: 0.0562 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9494 - val_segmentation_output_dice_coef: 0.9438\n",
      "Epoch 154/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1849 - classification_output_loss: 0.3495 - segmentation_output_loss: 0.0202 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9856 - segmentation_output_dice_coef: 0.9798 - val_loss: 0.2152 - val_classification_output_loss: 0.3742 - val_segmentation_output_loss: 0.0563 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9493 - val_segmentation_output_dice_coef: 0.9437\n",
      "Epoch 155/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1857 - classification_output_loss: 0.3513 - segmentation_output_loss: 0.0200 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9858 - segmentation_output_dice_coef: 0.9800 - val_loss: 0.2159 - val_classification_output_loss: 0.3752 - val_segmentation_output_loss: 0.0565 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9491 - val_segmentation_output_dice_coef: 0.9435\n",
      "Epoch 156/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1861 - classification_output_loss: 0.3521 - segmentation_output_loss: 0.0202 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9857 - segmentation_output_dice_coef: 0.9798 - val_loss: 0.2145 - val_classification_output_loss: 0.3713 - val_segmentation_output_loss: 0.0576 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9480 - val_segmentation_output_dice_coef: 0.9424\n",
      "Epoch 157/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1852 - classification_output_loss: 0.3502 - segmentation_output_loss: 0.0202 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9856 - segmentation_output_dice_coef: 0.9798 - val_loss: 0.2143 - val_classification_output_loss: 0.3725 - val_segmentation_output_loss: 0.0561 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9495 - val_segmentation_output_dice_coef: 0.9439\n",
      "Epoch 158/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1860 - classification_output_loss: 0.3520 - segmentation_output_loss: 0.0200 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9857 - segmentation_output_dice_coef: 0.9800 - val_loss: 0.2150 - val_classification_output_loss: 0.3737 - val_segmentation_output_loss: 0.0564 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9492 - val_segmentation_output_dice_coef: 0.9436\n",
      "Epoch 159/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1855 - classification_output_loss: 0.3507 - segmentation_output_loss: 0.0203 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9856 - segmentation_output_dice_coef: 0.9797 - val_loss: 0.2163 - val_classification_output_loss: 0.3762 - val_segmentation_output_loss: 0.0564 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9490 - val_segmentation_output_dice_coef: 0.9436\n",
      "Epoch 160/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1856 - classification_output_loss: 0.3509 - segmentation_output_loss: 0.0204 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9855 - segmentation_output_dice_coef: 0.9796 - val_loss: 0.2163 - val_classification_output_loss: 0.3760 - val_segmentation_output_loss: 0.0565 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9490 - val_segmentation_output_dice_coef: 0.9435\n",
      "Epoch 161/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1856 - classification_output_loss: 0.3511 - segmentation_output_loss: 0.0202 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9857 - segmentation_output_dice_coef: 0.9798 - val_loss: 0.2149 - val_classification_output_loss: 0.3735 - val_segmentation_output_loss: 0.0564 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9491 - val_segmentation_output_dice_coef: 0.9436\n",
      "Epoch 162/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1848 - classification_output_loss: 0.3498 - segmentation_output_loss: 0.0199 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9859 - segmentation_output_dice_coef: 0.9801 - val_loss: 0.2145 - val_classification_output_loss: 0.3730 - val_segmentation_output_loss: 0.0561 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9495 - val_segmentation_output_dice_coef: 0.9439\n",
      "\n",
      "Epoch 00162: ReduceLROnPlateau reducing learning rate to 7.999999797903002e-06.\n",
      "Epoch 163/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1848 - classification_output_loss: 0.3493 - segmentation_output_loss: 0.0202 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9857 - segmentation_output_dice_coef: 0.9798 - val_loss: 0.2155 - val_classification_output_loss: 0.3739 - val_segmentation_output_loss: 0.0571 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9484 - val_segmentation_output_dice_coef: 0.9429\n",
      "Epoch 164/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1854 - classification_output_loss: 0.3505 - segmentation_output_loss: 0.0202 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9856 - segmentation_output_dice_coef: 0.9798 - val_loss: 0.2158 - val_classification_output_loss: 0.3746 - val_segmentation_output_loss: 0.0570 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9486 - val_segmentation_output_dice_coef: 0.9430\n",
      "Epoch 165/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1848 - classification_output_loss: 0.3497 - segmentation_output_loss: 0.0200 - classification_output_acc: 1.0000 - segmentation_output_acc: 0.9858 - segmentation_output_dice_coef: 0.9800 - val_loss: 0.2161 - val_classification_output_loss: 0.3754 - val_segmentation_output_loss: 0.0569 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9486 - val_segmentation_output_dice_coef: 0.9431\n",
      "Epoch 166/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1849 - classification_output_loss: 0.3498 - segmentation_output_loss: 0.0200 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9858 - segmentation_output_dice_coef: 0.9800 - val_loss: 0.2158 - val_classification_output_loss: 0.3750 - val_segmentation_output_loss: 0.0566 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9489 - val_segmentation_output_dice_coef: 0.9434\n",
      "Epoch 167/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1847 - classification_output_loss: 0.3493 - segmentation_output_loss: 0.0200 - classification_output_acc: 1.0000 - segmentation_output_acc: 0.9858 - segmentation_output_dice_coef: 0.9800 - val_loss: 0.2158 - val_classification_output_loss: 0.3747 - val_segmentation_output_loss: 0.0568 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9487 - val_segmentation_output_dice_coef: 0.9432\n",
      "Epoch 168/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1851 - classification_output_loss: 0.3502 - segmentation_output_loss: 0.0199 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9858 - segmentation_output_dice_coef: 0.9801 - val_loss: 0.2158 - val_classification_output_loss: 0.3752 - val_segmentation_output_loss: 0.0565 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9490 - val_segmentation_output_dice_coef: 0.9435\n",
      "Epoch 169/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1853 - classification_output_loss: 0.3506 - segmentation_output_loss: 0.0200 - classification_output_acc: 0.9960 - segmentation_output_acc: 0.9858 - segmentation_output_dice_coef: 0.9800 - val_loss: 0.2159 - val_classification_output_loss: 0.3752 - val_segmentation_output_loss: 0.0566 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9489 - val_segmentation_output_dice_coef: 0.9434\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Epoch 170/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1848 - classification_output_loss: 0.3497 - segmentation_output_loss: 0.0198 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9859 - segmentation_output_dice_coef: 0.9802 - val_loss: 0.2160 - val_classification_output_loss: 0.3751 - val_segmentation_output_loss: 0.0569 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9487 - val_segmentation_output_dice_coef: 0.9431\n",
      "Epoch 171/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1860 - classification_output_loss: 0.3521 - segmentation_output_loss: 0.0199 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9858 - segmentation_output_dice_coef: 0.9801 - val_loss: 0.2161 - val_classification_output_loss: 0.3754 - val_segmentation_output_loss: 0.0567 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9488 - val_segmentation_output_dice_coef: 0.9433\n",
      "Epoch 172/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1856 - classification_output_loss: 0.3513 - segmentation_output_loss: 0.0199 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9858 - segmentation_output_dice_coef: 0.9801 - val_loss: 0.2151 - val_classification_output_loss: 0.3737 - val_segmentation_output_loss: 0.0564 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9491 - val_segmentation_output_dice_coef: 0.9436\n",
      "Epoch 173/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1852 - classification_output_loss: 0.3503 - segmentation_output_loss: 0.0200 - classification_output_acc: 0.9960 - segmentation_output_acc: 0.9858 - segmentation_output_dice_coef: 0.9800 - val_loss: 0.2149 - val_classification_output_loss: 0.3731 - val_segmentation_output_loss: 0.0567 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9488 - val_segmentation_output_dice_coef: 0.9433\n",
      "Epoch 174/200\n",
      "1000/1000 [==============================] - 35s 35ms/step - loss: 0.1851 - classification_output_loss: 0.3503 - segmentation_output_loss: 0.0200 - classification_output_acc: 0.9960 - segmentation_output_acc: 0.9858 - segmentation_output_dice_coef: 0.9800 - val_loss: 0.2149 - val_classification_output_loss: 0.3732 - val_segmentation_output_loss: 0.0567 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9489 - val_segmentation_output_dice_coef: 0.9433\n",
      "Epoch 175/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1867 - classification_output_loss: 0.3534 - segmentation_output_loss: 0.0199 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9858 - segmentation_output_dice_coef: 0.9801 - val_loss: 0.2146 - val_classification_output_loss: 0.3722 - val_segmentation_output_loss: 0.0569 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9486 - val_segmentation_output_dice_coef: 0.9431\n",
      "Epoch 176/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1845 - classification_output_loss: 0.3490 - segmentation_output_loss: 0.0200 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9858 - segmentation_output_dice_coef: 0.9800 - val_loss: 0.2143 - val_classification_output_loss: 0.3716 - val_segmentation_output_loss: 0.0570 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9486 - val_segmentation_output_dice_coef: 0.9430\n",
      "Epoch 177/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1846 - classification_output_loss: 0.3492 - segmentation_output_loss: 0.0201 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9858 - segmentation_output_dice_coef: 0.9799 - val_loss: 0.2149 - val_classification_output_loss: 0.3730 - val_segmentation_output_loss: 0.0569 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9487 - val_segmentation_output_dice_coef: 0.9431\n",
      "Epoch 178/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1849 - classification_output_loss: 0.3498 - segmentation_output_loss: 0.0199 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9859 - segmentation_output_dice_coef: 0.9801 - val_loss: 0.2151 - val_classification_output_loss: 0.3734 - val_segmentation_output_loss: 0.0568 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9487 - val_segmentation_output_dice_coef: 0.9432\n",
      "Epoch 179/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1851 - classification_output_loss: 0.3504 - segmentation_output_loss: 0.0197 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9860 - segmentation_output_dice_coef: 0.9803 - val_loss: 0.2153 - val_classification_output_loss: 0.3740 - val_segmentation_output_loss: 0.0567 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9487 - val_segmentation_output_dice_coef: 0.9433\n",
      "Epoch 180/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1852 - classification_output_loss: 0.3504 - segmentation_output_loss: 0.0199 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9858 - segmentation_output_dice_coef: 0.9801 - val_loss: 0.2156 - val_classification_output_loss: 0.3744 - val_segmentation_output_loss: 0.0567 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9488 - val_segmentation_output_dice_coef: 0.9433\n",
      "Epoch 181/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1858 - classification_output_loss: 0.3517 - segmentation_output_loss: 0.0200 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9858 - segmentation_output_dice_coef: 0.9800 - val_loss: 0.2160 - val_classification_output_loss: 0.3753 - val_segmentation_output_loss: 0.0567 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9488 - val_segmentation_output_dice_coef: 0.9433\n",
      "Epoch 182/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1850 - classification_output_loss: 0.3503 - segmentation_output_loss: 0.0198 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9860 - segmentation_output_dice_coef: 0.9802 - val_loss: 0.2151 - val_classification_output_loss: 0.3736 - val_segmentation_output_loss: 0.0565 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9490 - val_segmentation_output_dice_coef: 0.9435\n",
      "\n",
      "Epoch 00182: ReduceLROnPlateau reducing learning rate to 1.5999999959603884e-06.\n",
      "Epoch 183/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1853 - classification_output_loss: 0.3506 - segmentation_output_loss: 0.0201 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9858 - segmentation_output_dice_coef: 0.9799 - val_loss: 0.2152 - val_classification_output_loss: 0.3737 - val_segmentation_output_loss: 0.0567 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9489 - val_segmentation_output_dice_coef: 0.9433\n",
      "Epoch 184/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1844 - classification_output_loss: 0.3490 - segmentation_output_loss: 0.0199 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9859 - segmentation_output_dice_coef: 0.9801 - val_loss: 0.2152 - val_classification_output_loss: 0.3737 - val_segmentation_output_loss: 0.0567 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9488 - val_segmentation_output_dice_coef: 0.9433\n",
      "Epoch 185/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1853 - classification_output_loss: 0.3506 - segmentation_output_loss: 0.0200 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9858 - segmentation_output_dice_coef: 0.9800 - val_loss: 0.2153 - val_classification_output_loss: 0.3739 - val_segmentation_output_loss: 0.0567 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9488 - val_segmentation_output_dice_coef: 0.9433\n",
      "Epoch 186/200\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1854 - classification_output_loss: 0.3510 - segmentation_output_loss: 0.0198 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9860 - segmentation_output_dice_coef: 0.9802 - val_loss: 0.2154 - val_classification_output_loss: 0.3741 - val_segmentation_output_loss: 0.0568 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9488 - val_segmentation_output_dice_coef: 0.9432\n",
      "Epoch 187/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1847 - classification_output_loss: 0.3495 - segmentation_output_loss: 0.0198 - classification_output_acc: 1.0000 - segmentation_output_acc: 0.9859 - segmentation_output_dice_coef: 0.9802 - val_loss: 0.2155 - val_classification_output_loss: 0.3742 - val_segmentation_output_loss: 0.0567 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9488 - val_segmentation_output_dice_coef: 0.9433\n",
      "Epoch 188/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1860 - classification_output_loss: 0.3522 - segmentation_output_loss: 0.0198 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9860 - segmentation_output_dice_coef: 0.9802 - val_loss: 0.2156 - val_classification_output_loss: 0.3744 - val_segmentation_output_loss: 0.0569 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9486 - val_segmentation_output_dice_coef: 0.9431\n",
      "Epoch 189/200\n",
      "1000/1000 [==============================] - 35s 35ms/step - loss: 0.1853 - classification_output_loss: 0.3508 - segmentation_output_loss: 0.0198 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9859 - segmentation_output_dice_coef: 0.9802 - val_loss: 0.2156 - val_classification_output_loss: 0.3742 - val_segmentation_output_loss: 0.0569 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9486 - val_segmentation_output_dice_coef: 0.9431\n",
      "Epoch 190/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1853 - classification_output_loss: 0.3506 - segmentation_output_loss: 0.0199 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9858 - segmentation_output_dice_coef: 0.9801 - val_loss: 0.2156 - val_classification_output_loss: 0.3744 - val_segmentation_output_loss: 0.0569 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9486 - val_segmentation_output_dice_coef: 0.9431\n",
      "Epoch 191/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1862 - classification_output_loss: 0.3525 - segmentation_output_loss: 0.0199 - classification_output_acc: 0.9960 - segmentation_output_acc: 0.9859 - segmentation_output_dice_coef: 0.9801 - val_loss: 0.2158 - val_classification_output_loss: 0.3746 - val_segmentation_output_loss: 0.0570 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9486 - val_segmentation_output_dice_coef: 0.9430\n",
      "Epoch 192/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1845 - classification_output_loss: 0.3491 - segmentation_output_loss: 0.0198 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9859 - segmentation_output_dice_coef: 0.9802 - val_loss: 0.2157 - val_classification_output_loss: 0.3746 - val_segmentation_output_loss: 0.0568 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9487 - val_segmentation_output_dice_coef: 0.9432\n",
      "Epoch 193/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1858 - classification_output_loss: 0.3514 - segmentation_output_loss: 0.0202 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9856 - segmentation_output_dice_coef: 0.9798 - val_loss: 0.2158 - val_classification_output_loss: 0.3747 - val_segmentation_output_loss: 0.0568 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9487 - val_segmentation_output_dice_coef: 0.9432\n",
      "Epoch 194/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1845 - classification_output_loss: 0.3491 - segmentation_output_loss: 0.0200 - classification_output_acc: 0.9980 - segmentation_output_acc: 0.9858 - segmentation_output_dice_coef: 0.9800 - val_loss: 0.2157 - val_classification_output_loss: 0.3746 - val_segmentation_output_loss: 0.0568 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9487 - val_segmentation_output_dice_coef: 0.9432\n",
      "Epoch 195/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1863 - classification_output_loss: 0.3528 - segmentation_output_loss: 0.0199 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9859 - segmentation_output_dice_coef: 0.9801 - val_loss: 0.2156 - val_classification_output_loss: 0.3746 - val_segmentation_output_loss: 0.0566 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9489 - val_segmentation_output_dice_coef: 0.9434\n",
      "Epoch 196/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1854 - classification_output_loss: 0.3511 - segmentation_output_loss: 0.0198 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9860 - segmentation_output_dice_coef: 0.9802 - val_loss: 0.2156 - val_classification_output_loss: 0.3746 - val_segmentation_output_loss: 0.0566 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9489 - val_segmentation_output_dice_coef: 0.9434\n",
      "Epoch 197/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1856 - classification_output_loss: 0.3515 - segmentation_output_loss: 0.0198 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9860 - segmentation_output_dice_coef: 0.9802 - val_loss: 0.2158 - val_classification_output_loss: 0.3749 - val_segmentation_output_loss: 0.0566 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9489 - val_segmentation_output_dice_coef: 0.9434\n",
      "Epoch 198/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1853 - classification_output_loss: 0.3506 - segmentation_output_loss: 0.0199 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9859 - segmentation_output_dice_coef: 0.9801 - val_loss: 0.2159 - val_classification_output_loss: 0.3750 - val_segmentation_output_loss: 0.0567 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9488 - val_segmentation_output_dice_coef: 0.9433\n",
      "Epoch 199/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1849 - classification_output_loss: 0.3497 - segmentation_output_loss: 0.0201 - classification_output_acc: 0.9990 - segmentation_output_acc: 0.9857 - segmentation_output_dice_coef: 0.9799 - val_loss: 0.2160 - val_classification_output_loss: 0.3750 - val_segmentation_output_loss: 0.0569 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9486 - val_segmentation_output_dice_coef: 0.9431\n",
      "Epoch 200/200\n",
      "1000/1000 [==============================] - 36s 36ms/step - loss: 0.1851 - classification_output_loss: 0.3503 - segmentation_output_loss: 0.0199 - classification_output_acc: 0.9970 - segmentation_output_acc: 0.9859 - segmentation_output_dice_coef: 0.9801 - val_loss: 0.2159 - val_classification_output_loss: 0.3751 - val_segmentation_output_loss: 0.0567 - val_classification_output_acc: 0.9844 - val_segmentation_output_acc: 0.9488 - val_segmentation_output_dice_coef: 0.9433\n",
      "================================================ Saving model ================================================\n",
      "model saved to: MTL_classic.h5\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# print(\"================================================ begin training ================================================\")\n",
    "\n",
    "# hist = model.fit(train_x,\n",
    "#                  [train_c_y, train_s_y, train_s_y],\n",
    "#                  batch_size=batch_size,\n",
    "#                  epochs=200,\n",
    "#                  validation_data=(val_x, [val_c_y, val_s_y, val_s_y]),\n",
    "#                  verbose=1,\n",
    "#                  callbacks=[reduce_lr, csv_logger],\n",
    "#                  shuffle=True)\n",
    "\n",
    "# print(\"================================================ Saving model ================================================\")\n",
    "# model_filename = Name + \".h5\"\n",
    "# model.save_weights(model_filename)\n",
    "# print('model saved to:', model_filename)\n",
    "\n",
    "\n",
    "print(\"================================================ begin training ================================================\")\n",
    "\n",
    "hist = model.fit(train_x,\n",
    "                 [train_c_y, train_s_y],\n",
    "                 batch_size=batch_size,\n",
    "                 epochs=200,\n",
    "                 validation_data=(val_x, [val_c_y, val_s_y]),\n",
    "                 verbose=1,\n",
    "                 callbacks=[reduce_lr, csv_logger],\n",
    "                 shuffle=True)\n",
    "\n",
    "print(\"================================================ Saving model ================================================\")\n",
    "model_filename = Name + \".h5\"\n",
    "model.save_weights(model_filename)\n",
    "print('model saved to:', model_filename)\n",
    "\n",
    "\n",
    "# print(\"================================================ begin training ================================================\")\n",
    "\n",
    "# hist = model.fit([train_x,train_x],\n",
    "#                  [train_c_y, train_s_y],\n",
    "#                  batch_size=batch_size,\n",
    "#                  epochs=200,\n",
    "#                  validation_data=([val_x,val_x], [val_c_y, val_s_y]),\n",
    "#                  verbose=1,\n",
    "#                  callbacks=[reduce_lr, csv_logger],\n",
    "#                  shuffle=True)\n",
    "\n",
    "# print(\"================================================ Saving model ================================================\")\n",
    "# model_filename = Name + \".h5\"\n",
    "# model.save_weights(model_filename)\n",
    "# print('model saved to:', model_filename)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "28078269",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'seg-out_dice_coef'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_7848/3741794800.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"classification_output_acc\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"train_c_acc\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"segmentation_output_acc\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"train_s_acc\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"seg-out_dice_coef\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"train_s_dice\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;31m# plt.plot(N, hist.history[\"val_loss\"], label=\"val_loss\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'seg-out_dice_coef'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = np.arange(0, EPOCHS)\n",
    "plt.style.use(\"ggplot\")\n",
    "plt.figure()\n",
    "# plt.plot(N, hist.history[\"loss\"], label=\"train_loss\")\n",
    "plt.plot(N, hist.history[\"classification_output_loss\"], label=\"train_c_loss\")\n",
    "plt.plot(N, hist.history[\"segmentation_output_loss\"], label=\"train_s_loss\")\n",
    "\n",
    "plt.plot(N, hist.history[\"classification_output_acc\"], label=\"train_c_acc\")\n",
    "plt.plot(N, hist.history[\"segmentation_output_acc\"], label=\"train_s_acc\")\n",
    "plt.plot(N, hist.history[\"seg-out_dice_coef\"], label=\"train_s_dice\")\n",
    "\n",
    "# plt.plot(N, hist.history[\"val_loss\"], label=\"val_loss\")\n",
    "plt.plot(N, hist.history[\"val_classification_output_loss\"], label=\"val_c_loss\")\n",
    "plt.plot(N, hist.history[\"val_segmentation_output_loss\"], label=\"val_s_loss\")\n",
    "\n",
    "plt.plot(N, hist.history[\"val_classification_output_acc\"], label=\"val_c_acc\")\n",
    "plt.plot(N, hist.history[\"val_segmentation_output_acc\"], label=\"val_s_acc\")\n",
    "plt.plot(N, hist.history[\"val_seg-out_dice_coef\"], label=\"val_s_dice\")\n",
    "\n",
    "plt.title(\"Training Loss and Accuracy\")\n",
    "plt.xlabel(\"Epoch #\")\n",
    "plt.ylabel(\"Loss/Accuracy/DSC\")\n",
    "plt.legend()\n",
    "plt.savefig(os.path.join('output/', Name+'.png'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "452674d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"------Start predicting------\")\n",
    "\n",
    "\n",
    "\n",
    "predictions_c, predictions_s, predictions_s2 = model.predict(test_x, batch_size=32)\n",
    "\n",
    "\n",
    "print(\"------------------------------------------------ Classification testing ------------------------------------------------\")\n",
    "\n",
    "print(classification_report(test_c_y.argmax(axis=1), predictions_c.argmax(axis=1), target_names=lb.classes_, digits=6))\n",
    "\n",
    "print(\"------------------------------------------------ Segmentation testing ------------------------------------------------\")\n",
    "\n",
    "\n",
    "# evaluate the model\n",
    "# loss = model.evaluate(test_x, [test_c_y, test_s_y], verbose=0)\n",
    "loss, cla_loss, seg_loss, seg_loss2, cla_acc, seg_acc, seg_dice_coef = model.evaluate(test_x, [test_c_y, test_s_y, test_s_y], verbose=0)\n",
    "print('Test total loss:', loss)\n",
    "print('Test classification loss:', cla_loss)\n",
    "print('Test segmentation loss:', seg_loss)\n",
    "\n",
    "print('Test classification accuracy:', cla_acc)\n",
    "print('Test segmentation accuracy:', seg_acc)\n",
    "print('Test segmentation dice_coef:', seg_dice_coef)\n",
    "\n",
    "preds_c, preds_s, preds_s2 = model.predict(test_x, batch_size=8, verbose=1)\n",
    "test_mask = test_s_y.flatten()\n",
    "pred_mask = preds_s.flatten()\n",
    "fpr, tpr, thresholds = roc_curve(test_mask, pred_mask, pos_label=1)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "print(\"AUC:\", roc_auc)\n",
    "\n",
    "# Precision, Recall, accuracy, F1, IoU = p_r_f1_iou(test_s_y, preds_s)\n",
    "# print('Precision:', Precision)\n",
    "# print('Recall:', Recall)\n",
    "# print('m_Iou:', IoU)\n",
    "# print('F1_score:', F1)\n",
    "\n"
   ]
  },
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   "id": "2b1303f2",
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    "\n"
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   "execution_count": null,
   "id": "2959c28a",
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   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "id": "695c4f86",
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